CN101639858A - Image search method based on target area matching - Google Patents
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Abstract
The invention discloses an image search method based on target area matching, which comprises the following steps: a, selecting an image to be searched, and utilizing an edge detection algorithm to automatically partition the image to be searched; b, judging the partitioning effect, confirming a searched target area if a user satisfies the target area which is automatically partitioned, and selecting a target area that the user is interested in as a searched target from images that are not partitioned by self if the user does not satisfy the target area which is automatically partitioned; c, extracting characteristic values of the target area; d, taking the target area confirmed by the user as a template, using a template matching mode to carrying out template matching and similarity calculation in an image library, taking the searched image as a target when the similarity reaches a preset threshold value, or carrying out the template matching and the similarity calculation continuously until the image is covered completely; and e, sequentially displaying the searched result according to the similarity after the images in a database are searched according to requirements.
Description
Technical field
The present invention relates to image processing techniques, more particularly, relate to a kind of image search method based on target area matching.
Background technology
Fast development along with network technology, and the applying of multimedia technology, all there is every day thousands of picture to produce, how from the image library of magnanimity like this, finds the image of meeting consumers' demand quickly and accurately, become the key issue that field of image search presses for solution.
Traditional image retrieval mode adopts the text-based image retrieval technology.This technology is carried out text marking in the mode of artificial treatment to each width of cloth image before retrieving, describe picture material in the number of words scope of certain limitation, forms the mark database of describing image, to be labeled as key word, associates with image data base.During retrieval, in fact only the image labeling database is operated, carried out text matches, thereby reach the purpose that respective image is retrieved.Yet this text-based image retrieval technology exists serious problem.At first, present computer vision and artificial intelligence technology all can't mark image automatically, and must depend on manually image are made mark.This work is not only wasted time and energy, and manual mark is inaccurate often or incomplete, also has subjective deviation inevitably.That is to say that different people has different understanding methods to same width of cloth image, the difference of this subjective understanding will cause the mismatch error in the image retrieval.In addition, the abundant visual signature (color or texture etc.) that is comprised in the image often can't be described objectively with text.
In order to overcome these problems, CBIR technology (Content-Based Image Retrieval) is arisen at the historic moment.Promptly set up index and carry out similarity retrieval by the approximate measure between the characteristics of image vector according to color, texture, shape and their characteristic informations such as assemblage characteristic that image comprised.Yet, the most existing image retrieval algorithm all is to lay particular emphasis on to consider image overall information, fail effectively to utilize the local feature of image, though these methods have calculate simple, to translation with rotate advantages such as insensitive, but they can't describe picture material difference spatially, thus statistics similarity that can only the movement images overall situation, can not be on the object level similarity of movement images, thereby the semanteme of the image expression of feature similarity may be different, and recall precision is not high.Simultaneously, existing not only target selection mode of image search method underaction based on local message, and have the low problem of recall precision that characteristic index too much causes.
Therefore, how to solve the defective that is difficult to accurately express the user search intention in the conventional images retrieval, and improve recall precision and precision ratio is the technical barrier that needs to be resolved hurrily in the industry.
The invention provides a kind of image search method based on target area matching, effectively improved the precision ratio and the recall ratio of image retrieval, strengthened the dirigibility of image retrieval.
Summary of the invention
The purpose of this invention is to provide a kind of image search method based on target area matching, this method strengthens the precision ratio and the recall ratio of image retrieval when improving the image indexing system performance, and, easy to use.
The technical solution adopted for the present invention to solve the technical problems is: a kind of image search method based on target area matching is provided, and this method comprises the following steps:
A, image to be retrieved of selection utilize edge detection algorithm to treat retrieving images and cut apart automatically;
If b, judgement segmentation effect are the target area satisfaction of user to cutting apart automatically, the then target area of deterministic retrieval; If the user is dissatisfied to the target area of cutting apart automatically, then the user is not selecting interested target area as searching object in the split image voluntarily;
The eigenwert of c, extraction target area;
D, target area that the user is determined be as template, uses the template matches mode to carry out template matches in image library and similarity is calculated, when similarity reaches preset threshold value, then with this searched image as hit; Otherwise, proceed template matches and similarity and calculate, till image is covered fully;
E, the image in database are retrieved on request and are finished, and then show result for retrieval according to the similarity order.
Compared with prior art, the method for the present invention's proposition has the following advantages:
1, overcome the global statistics method and can not effectively improve the precision ratio and the recall ratio of image retrieval in the defective of the similarity of object level movement images;
2, not only the retrieval that can express the user on the mode more flexibly more exactly of choosing in the target area is intended to, and, choosing of characteristic index optimized, recall precision is improved greatly.
Description of drawings
The present invention will be described in detail below in conjunction with preferred embodiment and accompanying drawing, wherein:
Fig. 1 is the process flow diagram of the inventive method;
Fig. 2 is the schematic diagram of the inventive method coupling.
Embodiment
The image search method based on target area matching that the present invention proposes is cut apart automatically to image to be retrieved, or manually cut apart, the deterministic retrieval target area, extract color, texture and the shape facility value of image target area then, and these eigenwert indexs are optimized choose, adopt regional matching technique to carry out image retrieval at last.
The inventive method mainly is divided into the extraction of the choosing of target area, target area comprehensive characteristics and three processes of image retrieval of based target zone coupling, respectively the specific implementation of these processes is described below.
The image automated regional is cut apart and the specific implementation of choosing of area-of-interest: utilize the characteristics that always have the edge between two adjacent areas with different gray-scale values, the present invention uses the classical image partition method based on the border, with target in the image and target, or the border of target and background divides, and the user can select area-of-interest to retrieve as the target area.Because the edge is the discontinuous result of gray-scale value, this discontinuous derivative that utilizes detects easily.Simultaneously, consider the situation that the visually-perceptible of cut zone that dividing method obtains and human eye may there are differences, so whether the user can select interested target area in the split image voluntarily according to the satisfaction decision of automatic segmentation effect.
The specific implementation that the target area comprehensive characteristics extracts: after the user determines down the target area, extract the color, texture, shape information in zone eigenwert as the target area.
Color characteristic is with respect to further feature, and is highly stable, and for rotation, translation, dimensional variation, even various deformation is all insensitive, shows very strong robustness, and calculates simple.The present invention uses histogram to describe the most directly perceived this and tangible characteristics of image.Color histogram is a kind of method of color distribution in the presentation video, its transverse axis is represented color grade, the longitudinal axis is illustrated in has this color pixel shared ratio in entire image on some color grades, each scale in the histogram color space has been represented a kind of color in the color space.The present invention's selection meets HSV (Smith, the 1978) color model of people's visual response, adopts the higher cumulative histogram method of recall precision to come the color characteristic of token image.In the extraction of color characteristic, adopt unequal interval to quantize to colourity (Hue) in the HSV model, be that H is between [0-7], because the people is mainly determined by colourity H the sensation of color, in order to have reduced data volume and the complexity calculated, we only choose the color feature value of 8 characteristic quantities of colourity H as this width of cloth image, are designated as λ respectively
1, λ
2..., λ
8Though adopt the similarity between the histogram calculation image fairly simple, it can not reflect the space characteristics of object in the image.
Textural characteristics can be used to the spatial information in the image is carried out to a certain degree quantitative description.Image can be regarded the combination in different texture zone as, and texture is normally defined certain local property of image, is gray scale or the spatial coherence of color or the visual performance of gray scale and color space position variation of neighbor.Utilizing textural characteristics to carry out image retrieval is a kind of very effective means.It is a lot of to be used for the method for analysis image texture, but the method for analyzing texture roughly can be divided into following three kinds: statistic law, structure method and Spectrum Method.The present invention uses the gray level co-occurrence matrixes method in the statistic law to extract the textural characteristics of target area, and these characteristic variables comprise energy, moment of inertia, unfavourable balance square and entropy, are designated as λ respectively
9, λ
10, λ
11, λ
12Wherein, energy is the inhomogeneity tolerance of gradation of image; Moment of inertia is the amount of grey scale change in the dimensioned plan picture; The unfavourable balance square is local stationarity again, and it reflects the also local stability of image; Entropy is the tolerance of the quantity of information that has of image.
Shape facility is as the key character of object in the picture engraving and regional characteristics, it is the important means of describing high-rise visual information (as target, object etc.), and target, object are even more important to obtaining image, semantic, must have good shape facility to describe support with extraction algorithm.Shape facility comprises zone, major axes orientation, square, excentricity, circular rate, tangent angle etc.In addition, for figure, shape is its unique important feature.The edge of certain target is called profile in the image, and is an importance of Image Retrieval based on the retrieval of shape or profile, and the shape or the profile of the image that it can sketch the contours from the user are started with, and retrieve the similar image of shape in image library.The present invention uses the shape facility describing method based on rim detection to come shape facility is extracted, and square, excentricity, circular rate, tangent angle as the shape facility value, are designated as λ respectively
13, λ
14, λ
15, λ
16
The specific implementation of the image retrieval of based target zone coupling: the present invention uses template matching method to carry out image retrieval.Be about to the target area that the user determines and will be template, in searched image on translation and similarity coupling, when similarity reaches desirable threshold value, then this searched image is returned as hit.Otherwise, proceed translation and the work of similarity coupling, till image is covered fully.In retrieving, service range measure of the present invention carries out the coupling of similarity between the zone and calculates, and the distance definition of regional b and b ' is as follows:
Wherein, w
c, w
tAnd w
sThe weight of representative color feature, textural characteristics and shape facility, and w respectively
c+ w
t+ w
s=1.
As shown in Figure 1, the embodiment of image search method based on target area matching is as follows:
Step S100: searching system work, the user opens or selects a secondary image to be retrieved;
Step S101: utilize edge detection algorithm that image is cut apart automatically, with target in the image and target, or the border of target and background divides, and the user can select area-of-interest to retrieve as the target area.The algorithm of rim detection is a lot, adopts simple here and the reasonable Sobe1 operator of effect is finished rim detection;
Step S102: determine the target area that need are retrieved according to the satisfaction that the image automated regional is cut apart;
Step S103:, then choose the user's interest target area as searching object if the user is satisfied to the target area of cutting apart automatically;
Step S104: consider the situation that the visually-perceptible of cut zone that dividing method obtains and human eye may there are differences, if the user is dissatisfied to the target area of cutting apart automatically, then the user can not select interested target area as searching object in the split image voluntarily;
Step S105: extract the color, texture, shape information of target area eigenwert as the target area.Be specially: selection meets the hsv color model of people's visual response, adopts the higher cumulative histogram method of recall precision to come the color characteristic of token image.In the extraction of color characteristic, adopt unequal interval to quantize to colourity (Hue) in the HSV model, be that H is between [0-7], because the people is mainly determined by colourity H the sensation of color, in order to have reduced data volume and the complexity calculated, we only choose the color feature value of 8 characteristic quantities of colourity H as this width of cloth image, are designated as λ respectively
1, λ
2..., λ
8Simultaneously, use the gray level co-occurrence matrixes method in the statistic law to extract the textural characteristics of target area, these characteristic variables comprise energy, moment of inertia, unfavourable balance square and entropy, are designated as λ respectively
9, λ
10, λ
11, λ
12At last, use shape facility describing method to come shape facility is extracted, square, excentricity, circular rate, tangent angle as the shape facility value, are designated as λ respectively based on rim detection
13, λ
14, λ
15, λ
16
Step S106: use the template matches mode to retrieve, and the similarity of calculating and target area in image library, when similarity reaches preset threshold value, then with this searched image as hit;
Step S107: the image in database is retrieved on request and is finished, and then according to the similarity order, can be descending, or the mode of ascending order shows result for retrieval.
As shown in Figure 2, the template matches schematic diagram is: the target area that the user is determined is as matching template T, in searched image, with from left to right, order translation from top to bottom and similarity coupling, when similarity reached desirable threshold value, this returned this searched image as hit.Otherwise, proceed translation and the work of similarity coupling, till image is covered fully.With Fig. 2 is example, and this template is the rectangle of M * L pixel size, and template T overlays the search graph S upward translation of size for N * N pixel, and that block search figure under template covers is called subgraph S
Ij, (i j) is the coordinate of upper left corner picture point in S figure of this piece subgraph, and 0≤i≤N-M+1,0≤j≤N-L+1.16 comprehensive characteristics values and subgraph S according to template
IjCorresponding comprehensive characteristics value carry out similarity degree and calculate, establish b=T, b '=S
Ij, carry out similarity by following formula and calculate,
Wherein, w
c, w
tAnd w
sThe weight of representative color feature, textural characteristics and shape facility, and w respectively
c+ w
t+ w
s=1.
The present invention utilizes the image automated regional to cut apart the mode of choosing the target area that combines with area-of-interest, on the basis that the target area comprehensive characteristics is extracted, use the template matches mode to carry out picture search and similarity calculating, realized the image retrieval in based target zone, overcome the global statistics method and can not effectively improve the precision ratio and the recall ratio of image retrieval in the defective of the similarity of object level movement images.Simultaneously, comprehensive target area selection mode has strengthened the dirigibility and the convenience of image retrieval.
Claims (8)
1. an image search method based on target area matching is characterized in that, this method comprises the following steps:
A, image to be retrieved of selection utilize edge detection algorithm to treat retrieving images and cut apart automatically;
If b, judgement segmentation effect are the target area satisfaction of user to cutting apart automatically, the then target area of deterministic retrieval; If the user is dissatisfied to the target area of cutting apart automatically, then the user is not selecting interested target area as searching object in the split image voluntarily;
The eigenwert of c, extraction target area;
D, target area that the user is determined be as template, uses the template matches mode to carry out template matches in image library and similarity is calculated, when similarity reaches preset threshold value, then with this searched image as hit; Otherwise, proceed template matches and similarity and calculate, till image is covered fully;
E, the image in database are retrieved on request and are finished, and then show result for retrieval according to the similarity order.
2. the method for claim 1, it is characterized in that: edge detection algorithm utilizes gray-scale value to calculate among the step a.
3. the method for claim 1, it is characterized in that: the eigenwert of the target area of extracting among the step c comprises color and/or texture and/or shape information.
4. method as claimed in claim 3 is characterized in that: adopt color histogram to extract the color feature value of target area.
5. method as claimed in claim 3 is characterized in that: adopt statistic law, structure method or Spectrum Method to extract the textural characteristics value of target area.
6. method as claimed in claim 3 is characterized in that: adopt the gray level co-occurrence matrixes method in the statistic law to extract the textural characteristics of target area
7. method as claimed in claim 3 is characterized in that: adopt shape facility describing method to come shape facility is extracted based on rim detection, with square, excentricity, circular rate, tangent angle as the shape facility value.
8, the method for claim 1 is characterized in that: comprise that also the eigenwert index to extracting among the step c is optimized the step of choosing.
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