CN107506738A

CN107506738A - Feature extracting method, image-recognizing method, device and electronic equipment

Info

Publication number: CN107506738A
Application number: CN201710766531.5A
Authority: CN
Inventors: 杨茜; 牟永强
Original assignee: Shenzhen Intellifusion Technologies Co Ltd
Current assignee: Shenzhen Intellifusion Technologies Co Ltd
Priority date: 2017-08-30
Filing date: 2017-08-30
Publication date: 2017-12-22

Abstract

A kind of feature extracting method, methods described include：Obtain target image；The target image is divided at least two regions according to the upright direction of personage in the target image；Extract color information of each region in multiple color spaces；The color information of the multiple color spaces extracted from least two region is combined, obtains representing the assemblage characteristic vector of the target image.The present invention also provides a kind of feature deriving means, image-recognizing method and device, electronic equipment.The present invention can extract the feature of image exactly and carry out image recognition and tracking using this feature.

Description

Feature extracting method, image-recognizing method, device and electronic equipment

Technical field

The present invention relates to technical field of image processing, and in particular to a kind of feature extracting method, image-recognizing method, device And electronic equipment.

Background technology

With the development of computer technology, the application of image processing techniques is more and more, such as passes through image recognition technology To identify face, to be tracked personage etc. just need that personage is identified among these by picture charge pattern technology.And In person recognition, usually great changes have taken place for the posture of human body and action, therefore many features can not use.Feature extraction is image The important technical links for the treatment of technology, specifically, feature extraction handles image using computer extraction image information to be further (such as images match, picture charge pattern, person recognition) provides basis.If feature extraction it is sufficiently complete, abundant and inaccurate if Can not effective transmission figure picture information, follow-up image processing work is had an impact, for example, reduce follow-up image recognition, The accuracy and efficiency of picture charge pattern.Therefore, a kind of method for the feature for extracting image exactly is needed badly.

The content of the invention

In consideration of it, be necessary to provide a kind of feature extracting method, image-recognizing method, device and electronic equipment, can be accurate Extract the feature of image and utilize this feature to carry out image recognition and tracking in ground.

One aspect of the present invention provides a kind of feature extracting method, and the feature extracting method includes：

Obtain target image；

The target image is divided at least two regions according to the upright direction of personage in the target image；

Extract color information of each region in multiple color spaces；

The color information of the multiple color spaces extracted from least two region is combined, obtained described in expression The assemblage characteristic vector of target image.

In a kind of possible implementation, each region of extraction includes in the color information of multiple color spaces：

Each region is extracted in multiple color spaces according to color histogram and the information of each color dimension and uses feature Vector is indicated.

In a kind of possible implementation, the multiple color spaces that will be extracted from least two region Color information combination, obtain representing that the assemblage characteristic vector of the target image includes：

By each region in the information combination of each color dimension of each color space, each face in each region is obtained The assemblage characteristic vector of the colour space；

The assemblage characteristic vector of each color space in each region is spliced, obtains each region multiple The assemblage characteristic vector of color space；

Each region is spliced in the assemblage characteristic vector of multiple color spaces, the target image is obtained and exists The assemblage characteristic vector of multiple color spaces at least two region.

In a kind of possible implementation, the feature extracting method also includes：

Dimension-reduction treatment is carried out to the assemblage characteristic vector for representing the target image.

Another aspect of the present invention additionally provides a kind of image-recognizing method, and described image recognition methods includes：

Obtain target image；

Extract color information of each region in multiple color spaces；

The color information of the multiple color spaces extracted from least two region is combined, obtained described in expression The assemblage characteristic vector of target image；

Calculate the assemblage characteristic vector for representing the target image and sample image in image library characteristic vector it Between distance to judge the similitude in target image and described image storehouse between sample image；

The similitude highest sample image chosen in described image storehouse with the target image is image recognition result；

The target image is tracked according to described image recognition result.

In a kind of possible implementation, it is described calculate the assemblage characteristic vector for representing the target image with The distance between characteristic vector of sample image is to judge in target image and described image storehouse between sample image in image library Similitude before also include：

Another aspect of the present invention additionally provides a kind of feature deriving means, and described device includes：

Acquisition module, for obtaining target image；

Region division module, for being divided into the target image according to the upright direction of personage in the target image At least two regions；

Multiple color spaces processing module, for extracting color information of each region in multiple color spaces；

Feature representation module, for the color information for the multiple color spaces that will be extracted from least two region Combination, obtain representing the assemblage characteristic vector of the target image.

In a kind of possible implementation, the multiple color spaces processing module is specifically used for：According to color histogram Extract each region and the information of each color dimension and be indicated in multiple color spaces with characteristic vector.

In a kind of possible implementation, the feature representation module is specifically used for：

In a kind of possible implementation, the feature deriving means also include：

Dimensionality reduction module, for carrying out dimension-reduction treatment to the assemblage characteristic vector for representing the target image.

Another aspect of the invention additionally provides a kind of pattern recognition device, and described device includes：

Acquisition module, for obtaining target image；

Feature representation module, for the color information for the multiple color spaces that will be extracted from least two region Combination, obtain representing the assemblage characteristic vector of the target image；

Picture recognition module, the assemblage characteristic vector and image after dimensionality reduction for calculating the expression target image The distance between characteristic vector of sample image to be to judge the similitude between image in storehouse, choose in described image storehouse with it is described The similitude highest sample image of target image is image recognition result, according to described image recognition result to the target figure As being tracked.

In a kind of possible implementation, described image identification device also includes：

Dimensionality reduction module, for representing that the assemblage characteristic vector of the target image carries out dimension-reduction treatment.

Further aspect of the present invention also provides a kind of electronic equipment, and the electronic equipment includes memory and processor, described Memory is used to store at least one instruction, and the processor is used to realize features described above when performing the program stored in memory Extracting method and/above-mentioned image-recognizing method.

By above technical scheme, in of the invention, electronic equipment can obtain target image；According to people in the target image The target image is divided at least two regions by the upright direction of thing；Extract color of each region in multiple color spaces Information；The color information of the multiple color spaces extracted from least two region is combined, obtains representing the mesh The assemblage characteristic vector of logo image.Color is extracted by the multiple color spaces by target image zoning, and in each region Information, the color of image is expressed from multiple dimensions, has fully demonstrated the feature and distribution of color of image different piece, So that no matter the image under which kind of illumination condition can accurately represent image.Carried out by multizone and multiple color spaces Feature extraction it is achieved thereby that extract the purpose of the feature of image exactly.

The method that the present invention uses the area of space for the direction division image that personage is upright in target image, in each region In, the color information of multiple color spaces is extracted respectively, the stereogram of each color space is spliced into one-dimensional, is reconnected The histogram in the region different colours space, and the combination of the histogram acquisition multizone multiple color spaces of connection all areas Characteristic vector, so as to overcome the limitation in the Tracking Recognition of destination object, increase the information content of color space characteristic, together When efficient identification destination object spatial information.

Again by the assemblage characteristic vector dimensionality reduction of the multizone multiple color spaces of acquisition, to reduce operand and improve robust Property, come afterwards further according to the similitude between the assemblage characteristic vector calculating image of the multizone multiple color spaces of acquisition to target Image is identified and tracked.

Brief description of the drawings

Technical scheme in order to illustrate the embodiments of the present invention more clearly, it is required in being described below to embodiment to use Accompanying drawing is briefly described, it should be apparent that, drawings in the following description are some embodiments of the present invention, general for this area For logical technical staff, on the premise of not paying creative work, other accompanying drawings can also be obtained according to these accompanying drawings.

Fig. 1 is a kind of flow chart of feature extracting method provided in an embodiment of the present invention；

Fig. 2 is the exemplary plot that target image is divided at least two regions；

Fig. 3 is a kind of module map of feature deriving means provided in an embodiment of the present invention；

Fig. 4 is the schematic diagram of a kind of electronic equipment provided in an embodiment of the present invention.

Following embodiment will combine above-mentioned accompanying drawing and further illustrate the present invention.

Embodiment

It is below in conjunction with the accompanying drawings and specific real in order to be more clearly understood that the above objects, features and advantages of the present invention Applying example, the present invention will be described in detail.It should be noted that in the case where not conflicting, embodiments herein and embodiment In feature can be mutually combined.

Elaborate many details in the following description to facilitate a thorough understanding of the present invention, described embodiment only Only it is part of the embodiment of the present invention, rather than whole embodiments.Based on the embodiment in the present invention, ordinary skill The every other embodiment that personnel are obtained under the premise of creative work is not made, belongs to the scope of protection of the invention.

Unless otherwise defined, all of technologies and scientific terms used here by the article is with belonging to technical field of the invention The implication that technical staff is generally understood that is identical.Term used in the description of the invention herein is intended merely to description tool The purpose of the embodiment of body, it is not intended that in the limitation present invention.

Embodiment

Fig. 1 is the flow chart of feature extracting method provided in an embodiment of the present invention.Wherein, according to different demands, the stream The order of step can change in journey figure, and some steps can be omitted.As shown in figure 1, feature extracting method may include following step Suddenly：

S10：Obtain target image.

Above-mentioned target image is the image of feature extraction to be carried out.For example, carried out to the image captured to video camera Feature extraction, the then image captured video camera obtain the target image as target image.Meanwhile target image can To be original image that video camera captures or the original image that video camera captures is pre-processed (such as will Original image is cut) after obtained image.

Content in target image can include：Personage, animal, building, scenery etc., and the content in target image can With any combination including the above.

S11：The target image is divided at least two regions according to the upright direction of personage in the target image.

Upright personage's ratio is substantially similar, but because posture is different with action, meeting is divided by the upright direction of personage There is higher robustness, therefore target image is divided at least two regions according to the upright direction of personage in target image.

The upright direction of above-mentioned personage refers to the direction that people stands.For example, if the direction that people stands in target image is straight The Y direction of angular coordinate system, then the target image is divided at least two regions according to the Y direction of rectangular coordinate system.If The Y direction for the direction opposing right angles coordinate system that people stands is deviated to the right 45 degree in target image, then according to opposing right angles coordinate The target image is divided at least two regions by the direction that the Y direction of system is deviated to the right 45 degree.

Fig. 2 is referred to, Fig. 2 is the exemplary plot that target image is divided at least two regions.Wherein, personage in image 2a Upright direction is the Y direction of rectangular coordinate system, then is divided image 2a in the Y direction of rectangular coordinate system, image Represent that image 2a is divided into 5 regions by the Y direction in rectangular coordinate system in 2a by a dotted line.

Meanwhile in other embodiments, target image can also be drawn according to the upright direction of the personage in image It is divided at least two regions.Continuing with referring to Fig. 2, the upright direction of image 2b personage is the Y direction of rectangular coordinate system in Fig. 2, Then the upright direction of personage is X-direction in image, and image 2b is divided in the Y direction of rectangular coordinate system, Represent that image 2b is divided into 5 regions by the X-direction in rectangular coordinate system in image 2b by a dotted line.

In other embodiments, the vertical direction of personage in target image can not also be considered, only according to set in advance Direction is divided, such as image is carried out into region division according to a certain incline direction.Continuing with referring to Fig. 2, image 2c in Fig. 2 It is according to being in 45 degree of incline directions schematic diagram for being divided target image with the X-axis of rectangular coordinate system, passes through in image 2c Dotted line represents image 2c is being divided into 5 regions in 45 degree of incline directions with the X-axis of rectangular coordinate system.

In other embodiments, target image is carried out to the division in region according to spatial pyramid model.How according to sky Between pyramid model zoning can obtain from the prior art, therefore here is omitted.

Meanwhile it can also select aforesaid way that image is carried out into zoning either when target image does not include personage Other modes are by image zoning.

Because in the target image, when being divided according to the upright direction of personage, each region after division can include The information at the more position of personage, so being divided according to the upright direction of personage and then extracting feature can more react complete Image information.For example when being divided by the upright direction of personage, each region may include some of personage, such as head, personage The upper part of the body and personage's lower part of the body；If by vertically being divided with the upright direction of personage, a region may only include the one of personage Individual part, such as a region only include the head of personage, and another region only includes the upper limbs part of personage, and another region only includes people The foot of thing.

For example, the vertical direction of personage is the Y direction of rectangular coordinate system in image a and image b, image a is in red Clothing black trousers, image b is black jacket red trousers, if now being divided according to the X-direction of rectangular coordinate system, When color extraction is carried out after being divided to image a and image b, image a is similar with the color characteristic that image b is extracted, image a Identical image can be identified as with image b, and actual epigraph a and image the b image that to be two width different, the spy now extracted Levy it is not accurate enough, and then influence image recognition recognition result it is also inaccurate, reduce judgment accuracy.

Further, when carrying out region division to target image, target image can be divided into 3-5 parts.Divided More regions may increase the complexity of computing so as to reduce operation efficiency, and dividing that very few region may be not enough to completely will be every Individual image makes a distinction.Target image is divided into 3-5 region enough can partly to make a distinction the every of target image, but What will not be divided again is excessive.At zoning, every region can be the region of decile, or the not region of decile.

To each section can be marked after target image zoning, identified by marking ready-portioned each Region.Such as target image is divided into 3 parts, respectively region1, region2 and region3.

If not carrying out region division to target image, the dimensional orientation that possibly can not fully demonstrate object in target image is special Point, so as to reduce the accuracy of characterization image.For example, image c is the pedestrian of red jacket black cap, image d is red trousers The pedestrian of sub- black handbag, if only extracting colouring information, the color that image c and image d is extracted to image c and image d Feature is similar, and actual epigraph c and image the d image that to be two width different.Therefore, table is only not enough to by colouring information extraction The complete information of existing image, and need first to carry out image into the division in region.

S12：Extract color information of each region in multiple color spaces.

Above-mentioned color space is also referred to as color model, can be any number of color spaces in existing color space.Face The colour space includes but is not limited to RGB color, CMY color spaces, YUV color spaces, hsv color space, HIS colors sky Between, Lab color spaces.

As an alternative embodiment, in one embodiment, the multiple color space can include：RGB face The colour space, hsv color space, Lab color spaces.

I.e. multiple color spaces can be three color spaces or more than three color spaces, if three color skies Between, then respectively RGB color, hsv color space, Lab color spaces.

Wherein, RGB is the color space used in display, the three primary colors definition based on light.HSV is according to the straight of color A kind of color space that characteristic creates is seen, the parameter in this color space is respectively：Tone (H), saturation degree (S), brightness (V).Lab is the color space established on the basis of the color measurements international standard that International Commission on Illumination (CIE) formulates, and is A kind of and device-independent color space, and a kind of color system based on physiological characteristic, it is retouched with digitized mode State the visual response of people.Therefore, by extract RGB color, hsv color space, Lab color spaces color information can be with Closer to the visual experience of people so that digitized Color Expression is consistent with the impression of people.

For example, the color information for extracting each region in RGB color refers to：Each region is represented with three primary colors Colouring information a, specifically, pixel on each region can be represented by one group of R, G, B value.Extract each area Domain multiple color spaces color information, specifically by each region multiple color spaces color information with digitized Form is indicated, such as color information of each region in multiple color spaces is represented by characteristic vector.

As an alternative embodiment, in one embodiment, each region of extraction is in multiple color spaces Color information include：

Wherein, color histogram can in response diagram picture color composition distribution, that is, there is which color and various face The probability of occurrence of color, above-mentioned color dimension are also referred to as Color Channel either color component.

The information that each region each color dimension in multiple color spaces is extracted according to color histogram is specifically pair Multiple color spaces in each region carry out the extraction of the information of color dimension, represent to extract by characteristic vector The color characteristic of each color space arrived.The purpose being indicated by characteristic vector is for the ease of subsequently being calculated.

When being extracted according to color histogram, the color histogram in each region is first calculated.Calculate color histogram Need color space being divided into multiple small color intervals, each small color interval is referred to as a handle (bin) of histogram. This process is referred to as color quantizing.Then falling the pixel quantity in each small color interval by calculating color can obtain Color histogram.In general, the number of small color interval (bin) is more, and histogram is stronger to the resolution capability of color. However, the color histogram in a large number of handle can not only increase computation burden, also it is unfavorable for establishing rope in large-scale image library Draw.

In one embodiment of the invention, RGB color and the handle number in hsv color space are arranged to 8, Lab colors The handle number in space is arranged to 16.

For example, RBG is made up of tri- dimensions of R, G, B or three passages, each dimension sets 8 handles.HSV is by H, S, V Three dimensions are formed, and each dimension similarly sets 8 handles.By L (brightness), a, (color is from bottle green (low brightness values) to Lab Arrived again bright pink (high luminance values) to grey (middle brightness value)), b (color be from sapphirine (low brightness values) to grey (in it is bright Angle value) yellow (high luminance values) is arrived again) three dimensions form, each dimension sets 16 handles.

If target image is divided into these three regions of region1, region2 and region3, in a certain region (region1) information of each color dimension and it is expressed as in, in the RGB color extracted with characteristic vector：

[r₁, r₂, r₃, r₄, r₅, r₆, r₇, r₈]；[g₁, g₂, g₃, g₄, g₅, g₆, g₇, g₈]；[b₁, b₂, b₃, b₄, b₅, b₆, b₇, b₈]

Wherein, r₁Be region1 color histogram in a certain color interval a certain pixel R values or The average value of the R values of all pixels point of a certain color interval in region1 color histogram, or according to region1 Color histogram in a certain color interval presetted pixel point R value and the value that is calculated of preset formula.Likewise, g₁ Be region1 color histogram in a certain color interval a certain pixel G value or region1 color In the average value of the G values of all pixels point of a certain color interval in histogram, or color histogram according to region1 The value that the G of the presetted pixel point of a certain color interval value and preset formula is calculated.

Likewise, in the hsv color space extracted and Lab color spaces the information of each color dimension and with feature to Measuring expression is respectively：

[h₁, h₂, h₃, h₄, h₅, h₆, h₇, h₈]；[s₁, s₂, s₃, s₄, s₅, s₆, s₇, s₈]；[v₁, v₂, v₃, v₄, v₅, v₆, v₇, v₈]

[l₁, l₂, l₃..., l₁₄, l₁₅, l₁₆]；[a₁, a₂, a₃..., a₁₄, a₁₅, a₁₆]；[b₁, b₂, b₃..., b₁₄, b₁₅, b₁₆]

Wherein, each occurrence (such as h in characteristic vector₁And a₁) acquisition methods can be empty according to foregoing extraction RGB color Between in each color dimension information method it is consistent, or can according to extraction RGB color in each color dimension Information result and color space conversion formula changed, wherein color space conversion formula can be empty from existing color Between obtain in conversion formula.

S13：The color information of the multiple color spaces extracted from least two regions is combined, obtained described in expression The assemblage characteristic vector of target image.

Each region is being extracted after the color information of multiple color spaces, by multiple regions in multiple color spaces Color information is combined.For example, color information of each region in multiple color spaces is represented by characteristic vector, then will Multiple characteristic vectors are spliced, to obtain representing the assemblage characteristic vector of target image, or the mathematics for passing through kernel function Method is combined, and obtains representing the assemblage characteristic vector of target image.

As an alternative embodiment, in one embodiment, step S13 will carry from least two region The color information combination for the multiple color spaces got, obtains representing that the assemblage characteristic vector of the target image includes：

Wherein, each region is obtained in each region in the information combination of each color dimension of each color space The assemblage characteristic vector of each color space, as this region of region1, it can will represent that the region is empty in each color Between the characteristic vector of information of each color dimension spliced, obtain the characteristic vector of each color space in the region：

[r₁, r₂..., r₇, r₈, g₁, g₂..., g₇, g₈, b₁, b₂..., b₇, b₈]

[h₁, h₂..., h₇, h₈, s₁, s₂..., s₇, s₈, v₁, v₂..., v₇, v₈]

[l₁, l₂..., l₁₅, l₁₆, a₁, a₂..., a₁₅, a₁₆, b₁, b₂..., b₁₆]

Then, the assemblage characteristic vector of each color space in each region is spliced, i.e., by the institute in a region The characteristic vector for having color space is spliced into an assemblage characteristic vector, for this region of region1, can will represent each face The characteristic vector of the information of the colour space is spliced, and obtains assemblage characteristic vector of the region in multiple color spaces：

[r₁..., r₈, g₁..., g₈, b₁..., b₈, h₁..., h₈, s₁..., s₈, v₁..., v₈, l₁..., l₈, a₁..., a₈, b₁..., b₈]

=[RGB₁HSV₁LAB₁]

Each region after target image division can obtain the combination spy in multiple color spaces by above step Sign vector, then spliced assemblage characteristic vector of each region in multiple color spaces, obtain target image at least again The assemblage characteristic vector of multiple color spaces in two regions：

[Region1, Region2, Region3]

=[RGB₁HSV₁LAB₁, RGB₂HSV₂LAB₂, RGB₃HSV₃LAB₃]

By above step, the feature extraction to target image is completed, has obtained representing the assemblage characteristic of target image Vector.

As an alternative embodiment, in one embodiment, feature extracting method provided by the invention also includes： To representing that the assemblage characteristic vector of the target image carries out dimension-reduction treatment.

After obtaining representing the assemblage characteristic vector of target image, in order that expression formula is more succinct, it is easy to subsequently use, The assemblage characteristic vector for representing target image can be subjected to dimension-reduction treatment, to reduce computational complexity during follow-up use, improved Operation efficiency.

For example, dimension reduction method has principal component analysis (Principal Component Analysis, PCA), PCA can be passed through To representing that the assemblage characteristic vector of target image carries out dimensionality reduction.

The method of dimensionality reduction is not limited to above-mentioned PCA simultaneously, can also be that opposite feature elimination, number of combinations etc. arbitrarily drop Dimension method is to representing that the assemblage characteristic vector of target image carries out dimensionality reduction.

Further, in one embodiment, the group for the target image that feature extracting method provided by the invention is extracted Close characteristic vector and can be used for image recognition and tracking, be specially：

Calculate the assemblage characteristic vector for representing the target image and sample image in image library characteristic vector it Between distance to judge the similitude between image；

The target image is tracked according to described image recognition result.

Wherein it is possible to the expression before the vectorial either dimensionality reduction of the assemblage characteristic of the expression target image after calculating dimensionality reduction The distance between characteristic vector of sample image in the assemblage characteristic vector and image library of target image.

Preferably, the vectorial spy with sample image in image library of the assemblage characteristic for representing target image after dimensionality reduction is calculated The distance between vector is levied, can further improve the efficiency of computing, reduces computational complexity.

Wherein, the information of multiple sample images can be included in image library.For example, image library includes multiple sample images Title, numbering and characteristic vector etc..Such as the characteristic vector of title including party A-subscriber's image, numbering and party A-subscriber's image, pass through A The title of user images can in image library the unique mark sample, table can be used for by the characteristic vector of party A-subscriber's image Up to the feature of party A-subscriber's image.

Enter row distance calculate when, can be calculated by range formula, range formula include but is not limited to it is European away from From with a distance from, Ming Shi, mahalanobis distance.Which specifically used range formula can be selected as needed.Range formula can be from now Have in technology and obtained, here is omitted.There are the method for different determination similitudes, example for different range formulas Such as, the distance value obtained according to Euclidean distance is smaller, shows that the similarity between image is bigger.

Calculate represent target image assemblage characteristic vector and image library in sample image characteristic vector between away from From when, the assemblage characteristic vector and the characteristic vectors of multiple sample images in image library that represent target image can be carried out respectively Calculate, with acquisition and target image similarity highest image, so as to obtain image recognition result, i.e., whether wrapped in target image Containing a certain destination object.

For example, M images are carried out into feature extraction, obtain representing the assemblage characteristic vector x of M images, if image library includes A User images, party B-subscriber's image, C user images, the wherein characteristic vector of party A-subscriber's image are y_a, the characteristic vector of party B-subscriber's image For y_b, the characteristic vector of C user images is y_c, then characteristic vector x and y are calculated respectively according to Euclidean distance_a, characteristic vector x with y_b, characteristic vector x and y_cDistance.If characteristic vector x and y is calculated_bDistance it is minimum, identification party B-subscriber's image is and target Image is most like, and target image includes party B-subscriber.

After obtaining to the recognition result of target image, target image can be tracked according to image recognition result, Particularly the destination object recognized in target image is tracked.For example, the party B-subscriber in M images is tracked.

When carrying out image trace, existing image tracking algorithm can be selected to be tracked, here is omitted.

Assemblage characteristic vector due to representing target image is the result of feature extraction, can be used to indicate that target image, Therefore the content that target image includes can be identified and is tracked according to the result of feature extraction.

If the degree of accuracy of the feature extraction to target image is higher, image recognition and the degree of accuracy of image trace can be got over Height, if the degree of accuracy of feature extraction is not high, image recognition and the degree of accuracy of image trace can also reduce.

Meanwhile in addition to carrying out image recognition and image trace, other figures beyond image recognition can also be carried out As processing operation, for example, image retrieval, feature extraction for target image it is more accurate, then for subsequently carrying out other figures The degree of accuracy and efficiency during as processing operation is higher.

Feature extracting method provided in an embodiment of the present invention, obtain target image；It is straight according to personage in the target image The target image is divided at least two regions by vertical direction；The color that each region is extracted in multiple color spaces is believed Breath；The color information of the multiple color spaces extracted from least two region is combined, obtains representing the target The assemblage characteristic vector of image.Believed by the multiple color spaces extraction color by target image zoning, and in each region Breath, is expressed the color of image from multiple dimensions, has fully demonstrated the feature and distribution of color of image different piece, made Obtain no matter the image under which kind of illumination condition can accurately represent image.Carried out by multizone and multiple color spaces special Sign extraction it is achieved thereby that extract the purpose of the feature of image exactly.

Embodiment

The present invention also provides a kind of image-recognizing method, and described image recognition methods includes：

Obtain target image；

Extract color information of each region in multiple color spaces；

The target image is tracked according to described image recognition result.

As a preferred embodiment, the present invention is calculating the assemblage characteristic vector for representing the target image Before the distance between characteristic vector of sample image in image library, in addition to：To representing that the combination of the target image is special Sign vector carries out dimension-reduction treatment.

The method of the dimensionality reduction has principal component analysis (Principal Component Analysis, PCA), can pass through PCA is to representing that the assemblage characteristic vector of target image carries out dimensionality reduction.

Relevant description for image-recognizing method refer to the description of correlation step in features described above extracting method.Herein Repeat no more.

Embodiment

Fig. 3 is the structure chart of feature deriving means provided in an embodiment of the present invention, as shown in figure 3, feature deriving means can With including：Acquisition module 201, region division module 202, multiple color spaces processing module 203 and feature representation module 204.

Acquisition module 201, for obtaining target image.

Region division module 202, for drawing the target image according to the upright direction of personage in the target image It is divided at least two regions.

Further, when carrying out region division to target image, target image can be divided into 3-5 parts.Divided More regions may increase the complexity of computing so as to reduce operation efficiency, and dividing that very few region may be not enough to completely will be every Individual image makes a distinction.Target image is divided into 3-5 region enough can partly to make a distinction the every of target image, but What will not be divided again is excessive.At zoning, each region can be the region of decile, or the not region of decile.

Multiple color spaces processing module 203, for extracting color information of each region in multiple color spaces.

As an alternative embodiment, in one embodiment, multiple color spaces processing module 203 is specifically used for： Each region is extracted according to color histogram in multiple color spaces the information of each color dimension and to be carried out with characteristic vector Represent.

[l₁, l₂, l₃..., l_14,l₁₅, l₁₆]；[a₁, a₂, a₃..., a₁₄, a₁₅, a₁₆]；[b₁, b₂, b₃..., b₁₄, b₁₅, b₁₆]

Feature representation module 204, for the color for the multiple color spaces that will be extracted from least two region Information combination, obtain representing the assemblage characteristic vector of the target image.

As an alternative embodiment, in one embodiment, feature representation module 204 is specifically used for：

Wherein, feature representation module 204 by each region each color dimension of each color space information combination, The assemblage characteristic vector of each color space in each region is obtained, as this region of region1, can will represent the area Domain is spliced in the characteristic vector of the information of each color dimension of each color space, and it is empty to obtain each color in the region Between characteristic vector：

Then, feature representation module 204 is spliced the assemblage characteristic vector of each color space in each region, i.e., The characteristic vector in all colours space in one region is spliced into an assemblage characteristic vector, for this region of region1, The characteristic vector for the information for representing each color space can be spliced, the combination for obtaining the region in multiple color spaces is special Sign vector：

=[RGB₁HSV₁LAB₁]

Therefore, each region after target image division can obtain the assemblage characteristic vector in multiple color spaces, Feature representation module 204 is spliced assemblage characteristic vector of each region in multiple color spaces, is obtained target image and is existed The assemblage characteristic vector of multiple color spaces at least two regions：

[Region1, Region2, Region3]

=[RGB₁HSV₁LAB₁, RGB₂HSV₂LAB₂, RGB₃HSV₃LAB₃]

Feature extraction to target image is completed by feature representation module 204, has obtained representing the group of target image Close characteristic vector.

As an alternative embodiment, in one embodiment, feature deriving means provided by the invention also include：

As an alternative embodiment, in one embodiment, feature deriving means provided by the invention are extracted The assemblage characteristic vector of target image can be used for the identification and tracking of image, can be with when for image recognition and tracking Including：

The target image is tracked according to described image recognition result.

In the embodiment of the present invention, acquisition module obtains target image；Region division module is according to people in the target image The target image is divided at least two regions by the upright direction of thing；Multiple color spaces processing module is extracted each region and existed The color information of multiple color spaces；Multiple color spaces that feature representation module will be extracted from least two region Color information combination, obtain representing the assemblage characteristic vector of the target image.By by target image zoning, and The multiple color spaces extraction color information in each region, is expressed the color of image from multiple dimensions, is fully demonstrated The feature and distribution of color of image different piece so that no matter the image under which kind of illumination condition can accurately represent to scheme Picture.Feature extraction is carried out by multizone and multiple color spaces it is achieved thereby that extracting the purpose of the feature of image exactly.

The present invention divides the area of space of image according to the upright direction of personage in target image, in each area, point The color information of multiple color spaces is indescribably taken, the stereogram of each color space is spliced into one-dimensional, reconnects the area The histogram in domain different colours space, and the assemblage characteristic of the histogram acquisition multizone multiple color spaces of connection all areas Vector, so as to overcome the limitation in the Tracking Recognition of destination object, increase the information content of color space characteristic, at the same it is high Effect characterizes the spatial information of destination object.

Embodiment

The present invention also provides a kind of pattern recognition device, and described image identification device includes：

Acquisition module, for obtaining target image；

As a preferred embodiment, apparatus of the present invention also include：

Dimensionality reduction module can calculate the assemblage characteristic vector for representing the target image and sample graph in image library To representing that the assemblage characteristic vector of the target image carries out dimension-reduction treatment before the distance between characteristic vector of picture.

Relevant description for pattern recognition device refer to the description of correlation step in features described above extraction element.Herein Repeat no more.

Embodiment

Fig. 4 is refer to, Fig. 4 is the schematic diagram of electronic equipment 1 provided in an embodiment of the present invention.The electronic equipment 1 includes Memory 20, processor 30 and it is stored in the program 40 that can be run in the memory 20 and on the processor 30, example Such as feature extraction program.The processor 30 realizes the step in features described above extracting method embodiment when performing described program 40 Suddenly, step S10~S13 such as shown in Fig. 1；Or the processor 30 realizes said apparatus reality when performing described program 40 Apply the function of each module/unit in example, such as module 201~204.

Exemplary, described program 40 can be divided into one or more module/units, one or more of moulds Block/unit is stored in the memory 20, and is performed by the processor 30, to complete the present invention.It is one or more Individual module/unit can be the sequence of program instructions section that can complete specific function, and the instruction segment is used to describe described program 40 implementation procedure in the electronic equipment 1.For example, described program 40 can be divided into acquisition module 201 in Fig. 3, Region division module 202, multiple color spaces processing module 203 and feature representation module 204, each module concrete function is referring to foregoing Embodiment.

The electronic equipment 1 can be that desktop PC, notebook computer, palm PC and cloud server etc. calculate Machine equipment, it can also be embedding assembly equipment, such as video camera etc..It will be understood by those skilled in the art that the schematic diagram 4 The only example of electronic equipment 1, the restriction to electronic equipment 1 is not formed, can included than illustrating more or less portions Part, some parts or different parts are either combined, such as the electronic equipment 1 can also include input-output equipment, net Network access device, bus etc..

Alleged processor 30 can be CPU (Central Processing Unit, CPU), can also be Other general processors, digital signal processor (Digital SignalProcessor, DSP), application specific integrated circuit (Application Specific IntegratedCircuit, ASIC), ready-made programmable gate array (Field- Programmable Gate Array, FPGA) either other PLDs, discrete gate or transistor logic, Discrete hardware components etc..General processor can be microprocessor or the processor 30 can also be any conventional processor Deng the processor 30 is the control centre of the electronic equipment 1, utilizes various interfaces and the whole electronic equipment 1 of connection Various pieces.

The memory 20 can be used for storing said program 40 and/or module/unit, the processor 30 by operation or The computer program and/or module/unit being stored in the memory 20 are performed, and calls and is stored in memory 20 Data, realize the various functions of the electronic equipment 1.The memory 20 can mainly include storing program area and data storage Area, wherein, storing program area can store operation device, needed at least one function application program (such as sound-playing function, Image player function etc.) etc.；Storage data field can store uses created data (such as audio number according to electronic equipment 1 According to, video data etc.) etc..In addition, memory 20 can include high-speed random access memory, non-volatile deposit can also be included Reservoir, such as hard disk, internal memory, plug-in type hard disk, intelligent memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) block, flash card (Flash Card), at least one disk memory, flush memory device or other Volatile solid-state part.

If the integrated module/unit of the electronic equipment 1 is realized in the form of SFU software functional unit and as independent Production marketing in use, can be stored in a computer read/write memory medium.It is real based on such understanding, the present invention All or part of flow in existing above-described embodiment method, by program the hardware of correlation can also be instructed to complete, it is described Program can be stored in a computer-readable recording medium, the program when being executed by processor, can be achieved above-mentioned each side The step of method embodiment.Wherein, described program includes program code, and described program code can be source code form, object generation Code form, executable file or some intermediate forms etc..The computer-readable medium can include：Described program can be carried Any entity or device, recording medium, USB flash disk, mobile hard disk, magnetic disc, CD, computer storage, the read-only storage of code (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, electricity Believe signal and software distribution medium etc..It should be noted that the content that the computer-readable medium includes can be according to department Legislation and the requirement of patent practice carry out appropriate increase and decrease in method administrative area, such as in some jurisdictions, according to legislation and Patent practice, computer-readable medium do not include electric carrier signal and telecommunication signal.

In several embodiments provided by the present invention, it should be understood that disclosed method and apparatus, can also pass through Other modes are realized.For example, device embodiment described above is only schematical, for example, the division of the module, Only a kind of division of logic function, can there is other dividing mode when actually realizing.

Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention and it is unrestricted, although reference The present invention is described in detail for preferred embodiment, it will be understood by those within the art that, can be to the present invention's Technical scheme is modified or equivalent substitution, without departing from the spirit and scope of technical solution of the present invention.

Claims

1. a kind of feature extracting method, it is characterised in that methods described includes：

Obtain target image；

Extract color information of each region in multiple color spaces；

The color information of the multiple color spaces extracted from least two region is combined, obtains representing the target The assemblage characteristic vector of image.

2. the method as described in claim 1, it is characterised in that believe in the color of multiple color spaces in each region of extraction Breath includes：

Each region is extracted in multiple color spaces according to color histogram and the information of each color dimension and uses characteristic vector It is indicated.

3. method as claimed in claim 2, it is characterised in that it is described will be extracted from least two region it is multiple The color information combination of color space, obtains representing that the assemblage characteristic vector of the target image includes：

By each region in the information combination of each color dimension of each color space, it is empty to obtain each color in each region Between assemblage characteristic vector；

The assemblage characteristic vector of each color space in each region is spliced, obtains each region in multiple colors The assemblage characteristic vector in space；

Each region is spliced in the assemblage characteristic vector of multiple color spaces, obtains the target image described The assemblage characteristic vector of multiple color spaces at least two regions.

4. the method as described in claim 1, it is characterised in that methods described also includes：

5. a kind of image-recognizing method, it is characterised in that methods described includes：

Obtain target image；

Extract color information of each region in multiple color spaces；

The color information of the multiple color spaces extracted from least two region is combined, obtains representing the target The assemblage characteristic vector of image；

Calculate in the assemblage characteristic vector for representing the target image and image library between the characteristic vector of sample image Distance is to judge the similitude in target image and described image storehouse between sample image；

The target image is tracked according to described image recognition result.

6. image-recognizing method as claimed in claim 5, it is characterised in that calculate the expression target image described Assemblage characteristic vector and sample image in image library the distance between characteristic vector to judge target image and described image Also include before similitude in storehouse between sample image：

7. a kind of feature deriving means, it is characterised in that described device includes：

Acquisition module, for obtaining target image；

Region division module, for being divided into the target image at least according to the upright direction of personage in the target image Two regions；

Feature representation module, for the color information group for the multiple color spaces that will be extracted from least two region Close, obtain representing the assemblage characteristic vector of the target image.

8. feature deriving means as claimed in claim 7, it is characterised in that described device also includes：

9. a kind of pattern recognition device, it is characterised in that described device includes：

Acquisition module, for obtaining target image；

Feature representation module, for the color information group for the multiple color spaces that will be extracted from least two region Close, obtain representing the assemblage characteristic vector of the target image；

Picture recognition module, for calculate the assemblage characteristic vector after the dimensionality reduction for representing the target image with image library The distance between characteristic vector of sample image to judge the similitude between image, choose in described image storehouse with the target The similitude highest sample image of image is image recognition result, and the target image is entered according to described image recognition result Line trace.

10. device as claimed in claim 9, it is characterised in that described device also includes：

11. a kind of electronic equipment, it is characterised in that the electronic equipment includes memory and processor, and the memory is used for At least one instruction is stored, the processor is realized when being used to perform the program stored in memory as appointed in claim 1-4 One feature extracting method of meaning and/or such as claim 5-6 described image recognition methods.