CN108170849A - A kind of picture classification labeling method of zoned diffustion - Google Patents
A kind of picture classification labeling method of zoned diffustion Download PDFInfo
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- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
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Abstract
The invention discloses a kind of picture classification labeling methods of zoned diffustion, and corresponding tag along sort is automatically configured respectively including the picture to pre-save, and are obtained wherein the tag along sort being configured is chosen from preset at least one tag along sort.And it is with the process of accelerator classification marker by way of cutting combination is carried out to picture it also offers a kind of improved embodiment.
Description
Technical field
The invention belongs to field of image recognition, specifically, the present invention relates to a kind of picture classifications of zoned diffustion
Labeling method.
Background technology
With the development of society, between people interaction it is more and more, people can by mobile phone chats, send information with
And send picture etc..Simultaneously because cell-phone function is increasingly powerful, user passes through mobile phone photograph, all kinds of social networking applications and webpage
Browsing can obtain various photos and picture, lead to store a large amount of different types and interior in this way in the mobile phone of user
The picture of appearance.
When user browses the picture in mobile phone, date progress can only be obtained according to shooting sequence or picture substantially
Browsing, can not meet the needs of user easily browses particular picture.Simultaneously when needing to carry out taxonomic revision to picture, user
The taxonomic revision operation of picture can not be easily completed on mobile phone, can only be by after in picture bulk transfer to computer, then carry out
Taxonomic revision operates；Cause so more low to the efficiency of picture classification and browsing.And present existing picture classification side
The recognizer that method is taken is roughly the same but they have the problem of a similary, and being exactly cannot be effectively to figure when picture is larger
Piece is quickly identified, and when the too big picture of pixel is too big, very low for the very big processing speed of the consumption of hardware.Cause
This carries out multithreading and identifies the new technology for carrying out tag along sort simultaneously present applicant proposes picture is effectively divided
Scheme, to solve the problems of prior art.
Invention content
The present invention is directed at least solve one of technical problem in the prior art.For this purpose, the present invention provides a kind of point
The picture classification labeling method of area's diffusion, including：
Picture to pre-save automatically configures corresponding tag along sort respectively, wherein the tag along sort being configured is from advance
It chooses and obtains at least one tag along sort of setting.
Further, the described pair of picture pre-saved automatically configures corresponding tag along sort and specifically includes, by the figure
Piece is uniformly divided into AxB subregion, and image identification is then carried out to each subregion to obtain the tag along sort.
Further, it is described to automatically configure corresponding tag along sort and further include：
The image of the subregion adjacent to the AxB subregion progress divided is combined identification and obtains the tag along sort；
Wherein described combination identification includes, and selectes one of subregion, that is, xi,j, wherein i ∈ (1, A), i ∈ (1, A), then
From the subregion xi,jIt sets out, extends the subregion x to four directioni,jThe coloration of adjacent subregion expanded to is detected simultaneously
And/or gray scale, until the coloration or gray scale then stop continuing peritropous subregion extension more than a threshold value.
Further, the threshold value is the subregion xi,jWith being averaged for the coloration on the boundary line of adjacent sectors or gray scale
Value.
Description of the drawings
From following description with reference to the accompanying drawings it will be further appreciated that the present invention.Component in figure is not drawn necessarily to scale,
But it focuses on and shows in the principle of embodiment.In the figure in different views, identical reference numeral specifies correspondence
Part.
Fig. 1 is the classification marker flow chart of one embodiment of the present of invention.
Specific embodiment
In order to enable the objectives, technical solutions, and advantages of the present invention are more clearly understood, below in conjunction with attached drawing and its implementation
Example, the present invention will be described in further detail；It should be appreciated that specific embodiment described herein is only used for explaining this hair
It is bright, it is not intended to limit the present invention.To those skilled in the art, after access is described in detail below, the present embodiment
Other systems, method and/or feature will become obvious.It is intended to all such additional systems, method, feature and advantage
It is included in this specification, is included within the scope of the invention, and protected by the appended claims.In detailed below
Describe the other feature of the disclosed embodiments, and these characteristic roots according to it is described in detail below will be aobvious and easy
See.
Embodiment one.
The present embodiment provides a kind of picture classification labeling method of zoned diffustion, including：
Picture to pre-save automatically configures corresponding tag along sort respectively, wherein the tag along sort being configured is from advance
It chooses and obtains at least one tag along sort of setting.
Further, the described pair of picture pre-saved automatically configures corresponding tag along sort and specifically includes, by the figure
Piece is uniformly divided into AxB subregion, and image identification is then carried out to each subregion to obtain the tag along sort.
Further, it is described to automatically configure corresponding tag along sort and further include：
The image of the subregion adjacent to the AxB subregion progress divided is combined identification and obtains the tag along sort；
Wherein described combination identification includes, and selectes one of subregion, that is, xi,j, wherein i ∈ (1, A), i ∈ (1, A), then
From the subregion xi,jIt sets out, extends the subregion x to four directioni,jThe coloration of adjacent subregion expanded to is detected simultaneously
And/or gray scale, until the coloration or gray scale then stop continuing peritropous subregion extension more than a threshold value.
Further, the threshold value is the subregion xi,jWith being averaged for the coloration on the boundary line of adjacent sectors or gray scale
Value.
Picture recognition therein or the algorithm that uses of specific algorithm of combination identification for：
foreach image Xndo
The theme distribution of labels θ~Dirichlet (α) is sampled；θ is to tie up Dirichlet by the K of alpha parameter
Distribution
foreach label Yni of image Xndo
Z is distributed to themei~Multinominal (θ) is sampled
Always from theme zi'sIn take a label
For XnCalculate label priori：WhereinIt is YnMiddle yi
Quantity；ξ in training processni=0, η ＞ 0；ξ in test processni＞ 0, η ＞ 0.
To labels θ '~Dirichlet (| α 'n) theme distribution sampled；θ ' is by α 'nThe L dimensions of parametrization
Dirichlet is distributed
foreach instancexni ofXndo
V is distributed to labeli~Multinominal (θ ') is sampled
FromIn take an example；Label viC dimension multinomial
foreach tag tni in Tn of image Xndo
G is distributed to labeli~Multinominal (θ ') is sampled
From label gi'sIn take a label
In algorithm above, y={ y1,y2,...,yL, Y represents a set for having L label, with T={ t1,
t2,...,tTRepresent and have the set of T user identifier.With D={ ([X1,T1],Y1) ..., ([XN,TN],YN) represent one have
The training set of N number of sample, whereinBeing one has MnThe packet of a example, Being one has GnThe set of a user identifier, andIt is the L in Y setn
The set of a label.Can generate above one based on image (or image-region) instance X and user identifier T (if there is
If) learning machine that is labeled carries out cluster in visual signature space and establish a prototype set C={ c1,c2,...,
cC}.Wherein xiBe a size be C vector, wherein xi,cIt is that prototype c appears in xiIn number.Certainly it should be noted that
Above recognizer is only a kind of successful example, and in the practical application of method, it can be by those skilled in the art again
Other recognition methods are replaced, and the either primary object of the present embodiment or innovative point of the invention are the knowledge of zoned diffustion
The framework of the synchronous identification of other method, i.e. multithreading.
Embodiment two.
The present embodiment provides a kind of picture classification labeling method of zoned diffustion, including：
Picture to pre-save automatically configures corresponding tag along sort respectively, wherein the tag along sort being configured is from advance
It chooses and obtains at least one tag along sort of setting.
Further, the described pair of picture pre-saved automatically configures corresponding tag along sort and specifically includes, by the figure
Piece is uniformly divided into AxB subregion, and image identification is then carried out to each subregion to obtain the tag along sort, the subregion
Quantity carries out the distribution of ratio according to the aspect ratio of picture, and particular number is matched according to the hardware for the hardware device for implementing this method
It puts and is configured.
Further, it is described to automatically configure corresponding tag along sort and further include：
The image of the subregion adjacent to the AxB subregion progress divided is combined identification and obtains the tag along sort；
Wherein described combination identification includes, and selectes one of subregion, that is, xi,j, wherein i ∈ (1, A), i ∈ (1, A), then
From the subregion xi,jIt sets out, extends the subregion xi,jThe coloration and/or gray scale of adjacent subregion expanded to is detected simultaneously, directly
Extremely the coloration or gray scale then stop continuing peritropous subregion extension more than a threshold value, and the method for extension can use counterclockwise
The method of rotary expansion or simultaneously to subregion described in eight Directional Extensions.The selected of primary partition can be most intermediate by selecting
Subregion to set either simultaneously from four or multiple subregions being distributed on picture, can synchronize be combined point in this way
With making, the process that scoring area identifies is more more, improves the processing speed of this method.
Further, the threshold value is the subregion xi,jWith being averaged for the coloration on the boundary line of adjacent sectors or gray scale
Value can select threshold point in the way of " key point " in general image processing method.
Picture recognition therein or the algorithm that uses of specific algorithm of combination identification for：
foreach image Xndo
The theme distribution of labels θ~Dirichlet (α) is sampled；θ is to tie up Dirichlet by the K of alpha parameter
Distribution
foreach label Yni of image Xndo
Z is distributed to themei~Multinominal (θ) is sampled
Always from theme zi'sIn take a label
For XnCalculate label priori：WhereinIt is YnMiddle yi
Quantity；ξ in training processni=0, η ＞ 0；ξ in test processni＞ 0, η ＞ 0.
To labels θ '~Dirichlet (| α 'n) theme distribution sampled；θ ' is by α 'nThe L dimensions of parametrization
Dirichlet is distributed
foreach instance xni of Xndo
V is distributed to labeli~Multinominal (θ ') is sampled
FromIn take an example；Label viC dimension multinomial
foreach tag tni in Tn of image Xndo
G is distributed to labeli~Multinominal (θ ') is sampled
From label gi'sIn take a label
Embodiment three.
A kind of picture classification labeling method of the zoned diffustion of the present embodiment, each point including traversing configuration respectively first
Class label carries out image identification to the Target Photo under the tag along sort that currently traverses, is worked as according to image recognition result
Before marker feature under the tag along sort that traverses, it is right when some marker feature is comprised in multiple Target Photos
The feature of marker that each pictures in multiple described Target Photos are included is weighted or screens, currently to be traversed
The feature of marker under the tag along sort arrived, wherein, the tag along sort is associated with the marker feature without image, described
Marker feature includes the gray value at the key point position and the key point position of marker；
Object feature recognition is identified to acquired picture；The marker feature that acquired picture includes is calculated respectively
In key point position and gray value at key point position and the pass of the marker feature under each tag along sort of setting
The distance value of gray value at key point position and key point position determines the mark that the picture includes according to the distance value
The similarity of object feature and the marker feature under each tag along sort of setting, the marker that will be included with acquired picture
The similarity of feature meets the tag along sort of given threshold condition, is allocated to acquired picture, to complete to acquired figure
The classification of piece；All pictures got under same category label are stored in same file folder, while in acquisition
The thumbnail acceptance of the bid note tag along sort of picture.
Example IV.
The present embodiment provides a kind of picture classification labeling method, including：
Picture to pre-save automatically configures corresponding tag along sort respectively, wherein the tag along sort being configured is from advance
It chooses and obtains at least one tag along sort of setting.
Further, the described pair of picture pre-saved automatically configures corresponding tag along sort and specifically includes, by the figure
Piece is uniformly divided into AxB subregion, and image identification is then carried out to each subregion to obtain the tag along sort, the subregion
Quantity carries out the distribution of ratio according to the aspect ratio of picture, and particular number is matched according to the hardware for the hardware device for implementing this method
It puts and is configured.
Further, it is described to automatically configure corresponding tag along sort and further include：
The image of the subregion adjacent to the AxB subregion progress divided is combined identification and obtains the tag along sort；
Wherein described combination identification includes, and selectes one of subregion, that is, xi,j, wherein i ∈ (1, A), i ∈ (1, A), then
From the subregion xi,jIt sets out, extends the subregion xi,jThe coloration and/or gray scale of adjacent subregion expanded to is detected simultaneously, directly
Extremely the coloration or gray scale then stop continuing peritropous subregion extension more than a threshold value, and detection here can detect whole expansions
The whole coloration and/or gray scale for the subregion opened up, in the present embodiment by setting multiple detections on the propagation direction of subregion
Point, such as when being extended from a subregion to another subregion, uniformly detected along the direction of extension multiple points coloration and/
Or gray value is as detected value.
The method of extension can use the method for rotary expansion counterclockwise or simultaneously to subregion described in eight Directional Extensions.
Primary partition it is selected can by select most intermediate subregion set or simultaneously from four be distributed on picture or
The multiple subregions of person, can synchronize to be combined to distribute in this way makes the process of scoring area identification more more, improves the processing of this method
Speed.
Further, the threshold value is the subregion xi,jWith being averaged for the coloration on the boundary line of adjacent sectors or gray scale
Value can select threshold point in the way of " key point " in general image processing method.
Although the present invention is described by reference to various embodiments, but it is to be understood that do not departing from the present invention's above
In the case of range, many changes and modifications can be carried out.Therefore, be intended to foregoing detailed description be considered as it is illustrative and
It is unrestricted, and it is to be understood that following claims is intended to limit (including all equivalents) spirit and model of the present invention
It encloses.The above embodiment is interpreted as being merely to illustrate the present invention rather than limit the scope of the invention.It is reading
After the content of the record of the present invention, technical staff can make various changes or modifications the present invention, these equivalence changes and
Modification equally falls into the scope of the claims in the present invention.
Claims (4)
1. a kind of picture classification labeling method of zoned diffustion, which is characterized in that including：
Picture to pre-save automatically configures corresponding tag along sort respectively, wherein the tag along sort being configured is from presetting
At least one tag along sort in choose and obtain.
2. picture classification labeling method as described in claim 1, which is characterized in that the described pair of picture pre-saved is matched automatically
It puts corresponding tag along sort to specifically include, the picture is uniformly divided into AxB subregion, figure then is carried out to each subregion
Picture identifies to obtain the tag along sort.
3. picture classification labeling method as claimed in claim 2, which is characterized in that described to automatically configure corresponding tag along sort
It further includes：
The image of the subregion adjacent to the AxB subregion progress divided is combined identification and obtains the tag along sort；
Wherein described combination identification includes, and selectes one of subregion, that is, xi,j, wherein i ∈ (1, A), i ∈ (1, A), then from institute
State subregion xi,jIt sets out, extends the subregion x to multiple directionsi,jDetect simultaneously the adjacent subregion expanded to coloration and/or
Gray scale, until the coloration or gray scale then stop continuing peritropous subregion extension more than a threshold value.
4. picture classification labeling method as claimed in claim 3, which is characterized in that the threshold value is the subregion xi,jWith phase
The average value of coloration or gray scale on the boundary line of adjacent subregion.
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Cited By (1)
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| CN109376259A (en) * | 2018-12-10 | 2019-02-22 | 广东潮庭集团有限公司 | A kind of labeling method based on big data analysis |
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