CN107123127A - A kind of image subject extracting method and device - Google Patents

Abstract

The invention discloses an image subject extraction method and device, which relate to the field of image processing. The method includes: performing edge detection processing on an image to be processed based on an edge detection operator to obtain an image after edge detection; generating a detection sliding window, using a detection sliding window Sliding window detection is carried out in the image after edge detection; according to the number of pixels in the detection sliding window and the image subject recognition rules, the image subject area extraction process is performed in the image after edge detection, and the extracted image is used as the image to be processed main part of . The image subject extraction method and device of the present invention can greatly improve the processing efficiency of image subject detection and extraction, and do not need to accurately set foreground and background seed points to perform image solid color detection. For the image subject detection and extraction of pure color images The accuracy rate is high, so that the extracted image subject is accurate and reliable.

Description

Image subject extraction method and device

technical field

The present invention relates to the technical field of image processing, in particular to an image subject extraction method and device.

Background technique

In recent years, online shopping platforms have achieved considerable development. Online shopping platforms have accumulated a large amount of commodity image information. How to more effectively organize, analyze, retrieve and display commodity image information to consumers has become very important. The content of a product image includes the product body and background. When a user uploads a product image and expects to search for products that are the same or similar to the image, the user pays more attention to the product itself. Therefore, it is very important to extract the product body in the product image. a job. At present, image subject extraction methods are usually based on the Graph cuts algorithm. The main workflow of the Graph cuts algorithm is: set background seed points on the four corners of the image, execute the Graph cuts algorithm to obtain the subject mask, use sliding windows for detection, and obtain the image subject’s Margins and get the body's outer truncated rectangle. However, the Graph cuts algorithm is divided by pixels, and the execution efficiency is low. Moreover, the practice of setting the background seed point fixedly makes the subject occupy a large frame and cannot be successfully segmented when it overlaps with the background seed point.

Contents of the invention

In view of this, a technical problem to be solved by the present invention is to provide an image subject extraction method and device.

According to one aspect of the present invention, an image subject extraction method is provided, including: performing edge detection processing on an image to be processed based on an edge detection operator to obtain an edge-detected image; generating a detection sliding window, and using the detection sliding window in the Sliding window detection is carried out in the image after the edge detection; according to the number of pixels in the detection sliding window and the image subject recognition rule, the image subject area extraction process is performed in the image after the edge detection, and the extracted image is used as The main part of the image to be processed.

Optionally, acquire the color value of the pixel contained in the image to be processed, and determine whether the image to be processed is a solid color image according to the color value of the pixel and the image solid color judgment rule; if so, based on the The edge detection operator performs edge detection processing on the image to be processed; if not, the processing on the image to be processed ends.

Optionally, the acquiring the color value of the pixel contained in the image to be processed, and determining whether the image to be processed is a solid color image according to the color value of the pixel and an image solid color judgment rule includes: Processing the periphery of the image to set the detection limit, determining the image detection area within the detection limit; obtaining the color values of the pixels contained in the image detection area, and counting the pixels with the same color value included in the image detection area Proportion in all pixels of the pixel; obtain the color value and the proportion value with the highest proportion, and judge whether the proportion value is higher than the proportion threshold; if yes, then determine that the image to be processed is a solid color image.

Optionally, the generating a detection sliding window and using the detection sliding window to perform sliding window detection in the edge-detected image includes: respectively setting a horizontal sliding window and a vertical sliding window at the edge of the edge-detected image. Sliding window: controlling the horizontal sliding window and the vertical sliding window to slide towards the center of the image after edge detection respectively, so as to determine the boundary of the main body area of the image.

Optionally, the height pixel number of the horizontal sliding window is the width pixel number of the image after the edge detection, and the width pixel number of the horizontal sliding window is t1; the width pixel number of the vertical sliding window is the The length pixel number of the image after the edge detection, the height pixel number of the vertical sliding window is t2; wherein, control the horizontal sliding window, the vertical sliding window to the edge detection after the step t1 and t1 respectively The center of the image slides.

Optionally, when the horizontal sliding window and the vertical sliding window are set at the edge of the edge-detected image, interference regions are removed in the horizontal sliding window and the vertical sliding window, wherein the Interference areas include: trademark area.

Optionally, the performing image subject area extraction processing in the image after edge detection according to the number of pixels in the detection sliding window and image subject recognition rules includes: In the process of sliding the window to the center of the image after edge detection, the number of pixels contained in the horizontal sliding window, the vertical sliding window and the horizontal sliding window at the previous sliding position, the The difference d1 and d2 of the number of pixels contained in the vertical sliding window; calculate the difference dk1 of two d1 obtained continuously, when it is determined that dk1 is greater than the first pixel threshold, then stop the sliding of the horizontal sliding window and determine the The vertical boundary of the main body area of the image; calculate the difference dk2 of two d2 acquired continuously, and when it is determined that dk2 is greater than the second pixel threshold, stop the vertical sliding window and determine the horizontal boundary of the main body area of the image.

Optionally, before performing edge detection processing on the image to be processed based on the edge detection operator, a smoothing filter is used to perform filtering processing on the image to be processed; wherein the edge detection operator includes: Canny operator.

According to yet another aspect of the present invention, an image subject extraction device is provided, including: an edge detection module for performing edge detection processing on an image to be processed based on an edge detection operator to obtain an image after edge detection; a sliding window detection module for In generating the detection sliding window, the detection sliding window is used to perform sliding window detection in the image after the edge detection; the subject extraction module is used to identify the image subject according to the number of pixels in the detection sliding window and the image subject recognition rule. The main body area of the image is extracted from the image after the edge detection, and the extracted image is used as the main part of the image to be processed.

Optionally, the solid color determination module is configured to acquire the color value of the pixel contained in the image to be processed, and determine whether the image to be processed is a solid color image according to the color value of the pixel and an image solid color judgment rule; if The solid color determination module determines that the image to be processed is a solid color image, then the edge detection module performs edge detection processing on the image to be processed based on the edge detection operator; if the solid color determination module determines that the image to be processed is If the image is not a solid color image, the processing of the image to be processed ends.

Optionally, the pure color determination module is further configured to set a detection limit on the periphery of the image to be processed, determine an image detection area within the detection limit; acquire color values of pixels contained in the image detection area , counting the ratio of pixels with the same color value in all pixels included in the image detection area; obtaining the color value and the ratio with the highest ratio, and judging whether the ratio is higher than the ratio threshold ; If yes, then determine that the image to be processed is a solid color image.

Optionally, the sliding window detection module is further configured to respectively set a horizontal sliding window and a vertical sliding window at the edge of the edge detected image, and control the horizontal sliding window and the vertical sliding window to The center of the image after the edge detection is slid to determine the boundary of the main body area of the image.

Optionally, the height pixel number of the horizontal sliding window is the width pixel number of the image after the edge detection, and the width pixel number of the horizontal sliding window is t1; the width pixel number of the vertical sliding window is the The length pixel number of the image after edge detection, the height pixel number of the vertical sliding window is t2; the sliding window detection module is also used to control the horizontal sliding window and the vertical sliding window with step size t1 and t1 slides toward the center of the image after edge detection.

Optionally, the sliding window detection module is further configured to set the horizontal sliding window and the vertical sliding window at the edge of the image after the edge detection, when the horizontal sliding window and the vertical sliding window The interference area is removed from the window, wherein the interference area includes: a trademark area.

Optionally, the subject extraction module is further configured to respectively acquire the horizontal sliding window, the vertical sliding window, and the Differences d1 and d2 between the number of pixels contained in the vertical sliding window and the number of pixels contained in the horizontal sliding window and the vertical sliding window at the previous sliding position; calculate the difference between two d1 obtained continuously dk1, when it is determined that dk1 is greater than the first pixel threshold, then stop the horizontal sliding window sliding and determine the vertical boundary of the image subject area; calculate the difference dk2 of two d2 acquired continuously, when it is determined that dk2 is greater than the second pixel When the threshold is reached, stop the vertical sliding window from sliding and determine the horizontal boundary of the main body area of the image.

Optionally, the edge detection module is configured to use a smoothing filter to filter the image to be processed before performing edge detection processing on the image to be processed based on the edge detection operator; wherein, the The above-mentioned edge detection operators include: Canny operator.

According to still another aspect of the present invention, there is provided an image subject extraction device, comprising: a memory; and a processor coupled to the memory, the processor is configured to perform the above-mentioned operations based on instructions stored in the memory The image subject extraction method described above.

According to still another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium stores computer instructions, and when the instructions are executed by a processor, the image subject extraction described in any one of the above is realized. method.

The image subject extraction method and device of the present invention perform edge detection processing on the image based on the edge detection operator and extract the image subject through sliding window detection, which greatly improves the processing efficiency of image subject detection and extraction, and does not require accurate settings. Foreground and background seed points are limited, and the accuracy of image subject detection and extraction for solid-color images is high, making the extracted image subject accurate and reliable.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description These are only some embodiments of the present invention, and those skilled in the art can also obtain other drawings based on these drawings without any creative effort.

Fig. 1 is a schematic flow chart of an embodiment of an image subject extraction method according to the present invention;

FIG. 2 is a schematic flow diagram of another embodiment of the image subject extraction method according to the present invention;

3 is a schematic diagram of setting a solid color detection area in an embodiment of the image subject extraction method according to the present invention;

4A to 4E are schematic diagrams of determining the boundary of the image subject area through a sliding window in an embodiment of the image subject extraction method according to the present invention;

Fig. 5 is a schematic module diagram of an embodiment of an image subject extraction device according to the present invention;

Fig. 6 is a block diagram of another embodiment of an image subject extraction device according to the present invention.

detailed description

The present invention will be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments of the invention are illustrated. The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention. The technical solution of the present invention will be described in various aspects below in conjunction with various figures and embodiments.

The "first", "second" and so on in the following are only used to describe the difference, and have no other special meanings.

Fig. 1 is a schematic flow chart of an embodiment of the image subject extraction method according to the present invention, as shown in Fig. 1:

Step 101: Perform edge detection processing on the image to be processed based on an edge detection operator to obtain an edge-detected image.

The image to be processed can be a commodity image, etc., and the format can include: bmp, jpeg, png, etc. There are many kinds of edge detection operators, such as Laplacian operator, Canny operator and so on. In order to ensure the reliability of the edge detection process, the image to be processed is filtered by a smoothing filter before the edge detection process is performed on the image to be processed based on the edge detection operator.

A variety of smoothing filters can be used, such as a Gaussian filter, which generates a Gaussian filter template based on the Gaussian function and the filter scale σ, and uses the Gaussian filter template to perform convolution operations on the image to be processed, which can suppress image noise on the surface of the image to be processed , to make the edges of the image to be processed sharper.

Step 102, generate a detection sliding window, and use the detection sliding window to perform sliding window detection in the edge-detected image.

Step 103 , according to the detection of the number of pixels in the sliding window and the image subject recognition rule, the image subject area extraction process is performed in the image after edge detection, and the extracted image is used as the main part of the image to be processed.

The image subject extraction method and device provided in the above embodiments perform edge detection processing on the image based on the edge detection operator and extract the image subject through sliding window detection. It is not necessary to accurately set the seed points of the foreground and background, which improves the detection and extraction. processing efficiency.

Fig. 2 is a schematic flow chart of another embodiment of the image subject extraction method according to the present invention, as shown in Fig. 2:

Step 201, performing solid color detection on the image to be processed.

Solid color detection can be done in a number of ways. For example, the color value of the pixel contained in the image to be processed is obtained, and whether the image to be processed is a solid color image is determined according to the color value of the pixel and the image solid color judgment rule. Trademarks, introductions, addresses, etc. often appear on the outer edge of the image to be processed (such as the actual product image). During the detection, it is necessary to eliminate the interference of trademarks, introductions, etc., and set the detection area to improve the accuracy of detection.

As shown in Figure 3, the specified pixels are indented from the outermost edge of the image to form an image detection area 32 and a blank area 31. The outermost edge of the image is indented inward to avoid trademarks, Interference with introductions, addresses, etc. The pixels that the outermost edge of the image shrinks inward can be set according to different images, for example, 10 pixels, etc., which means that the outermost edge of the image shrinks inward by 10 pixels.

In one embodiment, a detection limit is set on the periphery of the image to be processed, and an image detection area within the detection limit is determined. Obtain the color value of the pixels contained in the image detection area, count the proportion of pixels with the same color value among all the pixels contained in the image detection area, obtain the color value with the highest proportion and the proportion value, and judge the proportion value Whether it is higher than the proportion threshold, if yes, determine that the image to be processed is a solid color image.

For example, traverse the pixels included in the image detection area 32, construct 256 color histograms (256 buckets, the range of color values is [0-255]), count the pixels with the same color value in the image detection area 32, each A histogram represents the number of pixels with a color. After the statistics are completed, sort by the height of the histogram (the height of each histogram represents the total number of pixels with the color corresponding to the histogram) to obtain the highest histogram.

For example, the No. 0 histogram is the highest (pixel value 0 represents black), which means that there are the most black pixels in the image detection area 32, and the pixel points that are black in the image detection area 32 are calculated according to the height of all histograms in the image detection area 32. The proportion of all pixels in the area 32. The proportion threshold R can be preset, for example, 80%.

If the number of black pixels in the image detection area 32 exceeds 80% of all pixels in the image detection area 32, it means that the image to be processed is a solid color image, that is, the image to be processed has the characteristics of a solid background color. If the image has the characteristics of a solid background color, the accuracy of extracting the image subject by using the image subject extraction method of the present invention is high.

Step 202, determine whether the image to be processed is a solid color image, if yes, go to step 204, if not, go to step 203, exit the operation, and end the processing of the to-be-processed image.

Step 204, performing edge detection processing on the image to be processed based on the Canny operator.

Canny operator is a multi-stage optimization operator with filtering, enhancement and detection functions. Before processing, the Canny operator uses a Gaussian smoothing filter to smooth the image to remove noise. The Canny segmentation algorithm uses the finite difference of the first-order partial derivative to calculate the gradient magnitude and direction, suppresses the non-maximum value of the gradient magnitude, uses the double threshold algorithm to detect and connect the edges, and searches for the edges of the image to be processed. The edge detection algorithm of the Canny operator is used to output the boundaries of different color regions with contour lines, and the edge contour lines of the image to be processed can be displayed in the image after edge detection. The edge detection algorithm of the Canny operator can separate the subject and the background (different Color regions) are output as contour lines.

Step 205, respectively setting a horizontal sliding window and a vertical sliding window at the edge of the image after edge detection.

Four sliding windows with fixed sizes are respectively taken at the four edges of the image after edge detection. The horizontal sliding window is a sliding window that slides in the horizontal direction, and the vertical sliding window is a sliding window that slides in the vertical direction. The height of the horizontal sliding window is rounded to the height of the image, and the width is fixed (according to the set threshold t1). The height pixels of the horizontal sliding window are the width pixels of the image after edge detection, and the width pixels of the horizontal sliding window are t1 . The height of the vertical sliding window is rounded to the whole image width, and the height is taken as a fixed size (according to the set threshold t2). The width pixels of the vertical sliding window are the length pixels of the image after edge detection, and the height pixels of the vertical sliding window are t2.

In one embodiment, when a horizontal sliding window and a vertical sliding window are set at the edge of the image after edge detection, it is necessary to remove the interference area in the horizontal sliding window and the vertical sliding window, and the interference area includes

Including: trademark area, introduction area, etc. The purpose of removing the interference area is to reduce the inaccuracy of image subject detection caused by the contour line (edge detection result) of the interference area.

For example, a trademark often appears in the upper left corner of an e-commerce product image, with a height of about a quarter of the height of the product image and a width of about one third of the width of the product image. In order to uniformly eliminate the interference of the trademark position in the upper left corner, among the four sliding windows set above, a certain height is cut on the top of the horizontal sliding window on the left (the number of pixels of the height of the clipping can be set), and the vertical sliding window at the top Crop a certain width on the left (the number of pixels of the cropped width can be set).

Step 206, controlling the horizontal sliding window and the vertical sliding window to slide towards the center of the edge-detected image respectively, and determining the boundary of the main body area of the image according to the number of pixels contained in the sliding window and the pixel threshold.

The image subject recognition rule may have various rules. For example, the horizontal sliding window and the vertical sliding window are controlled to slide toward the center of the edge-detected image with steps t1 and t1 respectively. In the process of sliding the horizontal sliding window and vertical sliding window to the center of the image after edge detection, obtain the number of pixels contained in the horizontal sliding window and vertical sliding window and the horizontal sliding window and vertical sliding window at the previous sliding position The difference between the number of pixels contained in d1 and d2.

Calculate the difference dk1 between two d1 acquired continuously, and when it is determined that dk1 is greater than the first pixel threshold, stop the horizontal sliding window and determine the vertical boundary of the image main body area. Calculate the difference dk2 between two d2 obtained continuously, and when it is determined that dk2 is greater than the second pixel threshold, stop the vertical sliding window and determine the horizontal boundary of the image main body area.

As shown in FIG. 4A , the horizontal sliding window on the left can be regarded as a sliding rectangular area with the height of the image as the height and a fixed width of 2 pixels. Sliding with a step size of 2 pixels, the horizontal coordinate position of the upper left corner of the horizontal sliding window is 0 at the first moment, and the horizontal coordinate position is 2 at the next moment. Every time the horizontal sliding window slides one step, calculate the difference d between the number of pixels in the horizontal sliding window and the number of pixels in the horizontal sliding window in the previous step.

For example, since after the edge detection algorithm is executed, the image to be detected turns into an image that only shows the outline lines of the subject, and the color of the lines is white, then calculate the number of white pixels in the horizontal sliding window and the white color in the horizontal sliding window in the previous step The difference in number of pixels d.

dk is recorded as the difference between the last two consecutive d, when it is judged that dk>threshold a, stop sliding, calculate the sliding distance of the horizontal sliding window, and obtain the distance from the left side of the outer truncated rectangle of the image subject to the edge of the image. The arrow shows the position where dk>threshold a is determined, that is, the horizontal sliding window slides 4 steps, and the step size is 2 pixels, then the margin from the left side of the outer truncated rectangle of the image main body to the edge of the image is 8 pixels.

As shown in Figure 4B, the arrow shows the position where dk>threshold a is determined, that is, the vertical sliding window slides for 3 steps, and the step size is 2 pixels, then the distance from the upper side of the outer truncated rectangle of the image body to the edge of the image is 6 pixels. As shown in Figure 4C, the arrow shows the position where dk>threshold a is determined, that is, the horizontal sliding window slides for 3 steps, and the step size is 2 pixels, then the distance from the right side of the outer truncated rectangle of the image body to the edge of the image is 6 pixels. As shown in Figure 4D, the arrow shows the position where dk>threshold a is determined, that is, the vertical sliding window slides for 3 steps, and the step size is 2 pixels, then the distance from the bottom edge of the outer truncated rectangle of the image body to the edge of the image is 6 pixels. As shown in FIG. 4E , the main body area of the image surrounded by four truncated rectangular sides of the main body of the image is obtained, and the image extracted from the main body area of the image is used as the main part of the image to be processed.

According to experiments, the efficiency of image subject detection and extraction by using the image subject extraction method of the present invention is greatly improved. For example, taking a 400x400 commodity image as an example, under the experimental conditions of Mac OSX, 2.6GHz Intel Core i5, and single thread, it takes about 1869 milliseconds to perform Graph cuts segmentation, but under the same experimental conditions, using the image subject extraction method of the present invention method, it only takes about 35 milliseconds to perform image subject detection and extraction on the same image.

The image subject extraction method and device provided by the above embodiments greatly improve the processing efficiency of image subject detection and extraction, and are not limited by the existing Graph Cuts algorithm that relies on interaction and needs to accurately set the foreground and background seed points. For pure color The image subject detection and extraction accuracy of the image is high.

In one embodiment, as shown in FIG. 5 , the present invention provides an image subject extraction device 50 , including: an edge detection module 51 , a sliding window detection module 52 , a subject extraction module 53 and a solid color determination module 54 .

The edge detection module 51 performs edge detection processing on the image to be processed based on an edge detection operator to obtain an edge-detected image. The edge detection module 51 uses a smoothing filter to perform filtering processing on the image to be processed before performing edge detection processing on the image to be processed based on an edge detection operator; wherein, the edge detection operator includes: Canny operator and the like.

The sliding window detection module 52 generates a detection sliding window, and uses the detection sliding window to perform sliding window detection in the edge-detected image. The subject extraction module 53 performs image subject area extraction processing in the image after edge detection according to the number of pixels in the detection sliding window and image subject recognition rules, and takes the extracted image as the main part of the image to be processed.

The solid color determination module 54 acquires the color values of the pixels contained in the image to be processed, and determines whether the image to be processed is a solid color image according to the color values of the pixels and the image solid color judgment rule. If the solid color determination module 54 determines that the image to be processed is a solid color image, the edge detection module 51 performs edge detection processing on the image to be processed based on an edge detection operator. If the solid-color determining module 54 determines that the image to be processed is not a solid-color image, the processing of the image to be processed ends.

The pure color determining module 54 sets a detection limit on the periphery of the image to be processed, and determines an image detection area within the detection limit. The pure color determination module 54 acquires the color values of the pixels included in the image detection area, and counts the proportion of pixels with the same color value in all the pixels included in the image detection area. The solid color determination module 54 acquires the color value with the highest proportion and the proportion value, and judges whether the proportion value is higher than the proportion threshold, and if so, determines that the image to be processed is a solid color image.

Sliding window detection module 52 sets horizontal sliding window and vertical sliding window respectively at the edge of the image after edge detection, and controls horizontal sliding window and vertical sliding window to slide to the center of the image after edge detection respectively, in order to determine the area of the main body area of the image. boundary. The height pixel number of the horizontal sliding window is the width pixel number of the image after the edge detection, and the width pixel number of the horizontal sliding window is t1. The width pixels of the vertical sliding window are the length pixels of the image after edge detection, and the height pixels of the vertical sliding window are t2.

When the sliding window detection module 52 sets the horizontal sliding window and the vertical sliding window at the edge of the image after the edge detection, the interference area is removed from the horizontal sliding window and the vertical sliding window, wherein the interference area includes: a trademark area. The sliding window detection module 52 controls the horizontal sliding window and the vertical sliding window to slide towards the center of the edge-detected image with steps t1 and t1 respectively.

Subject extraction module 53 obtains respectively the number of pixels contained in the horizontal sliding window and the vertical sliding window and the horizontal sliding window at the last sliding position during the sliding process of the horizontal sliding window and the vertical sliding window to the center of the image after edge detection. , the difference d1 and d2 of the number of pixels included in the vertical sliding window. The subject extraction module 53 calculates the difference dk1 between two d1 obtained continuously, and when it is determined that dk1 is greater than the first pixel threshold, the horizontal sliding window is stopped and the vertical boundary of the main body area of the image is determined. The subject extraction module 53 calculates the difference dk2 between two d2 acquired continuously, and when it is determined that dk2 is greater than the second pixel threshold, the vertical sliding window is stopped and the horizontal boundary of the subject area of the image is determined.

Fig. 6 is a block diagram of another embodiment of an image subject extraction device according to the present invention. As shown in FIG. 6 , the device may include a memory 61 , a processor 62 , a communication interface 63 and a bus 64 . The memory 61 is used to store instructions, and the processor 62 is coupled to the memory 61 , and the processor 62 is configured to implement the above image subject extraction method based on the instructions stored in the memory 61 .

The memory 61 can be a high-speed RAM memory, a non-volatile memory (non-volatile memory), etc., and the memory 61 can also be a memory array. The memory 61 may also be partitioned, and the blocks may be combined into virtual volumes according to certain rules. The processor 62 may be a central processing unit CPU, or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the image subject extraction method of the present invention.

In one embodiment, the present invention provides a computer-readable storage medium. The computer-readable storage medium stores computer instructions. When the instructions are executed by a processor, the image subject extraction method in any one of the above embodiments is implemented.

The image subject extraction method and device provided in the above-mentioned embodiments perform edge detection processing on the image based on the edge detection operator and extract the image subject through sliding window detection, which greatly improves the processing efficiency of image subject detection and extraction, and does not Due to the limitation that the existing Graph Cuts algorithm relies on interaction and needs to accurately set the foreground and background seed points, the image solid color detection is performed, and the accuracy of the image subject detection and extraction of the pure color image is higher, so that the extracted image subject is accurate and reliable.

It is possible to implement the methods and systems of the present invention in many ways. For example, the method and system of the present invention may be implemented by software, hardware, firmware or any combination of software, hardware, and firmware. The above sequence of steps used in the method is for illustration only, and the steps of the method of the present invention are not limited to the sequence described above unless specifically stated otherwise. Furthermore, in some embodiments, the present invention can also be implemented as programs recorded in recording media including machine-readable instructions for realizing the method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.

The description of the present invention has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and changes will be apparent to those of ordinary skill in the art. The embodiment was chosen and described in order to better explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention and design various embodiments with various modifications as are suited to the particular use.

Claims (18)

1. a kind of image subject extracting method, it is characterised in that including：

Edge detection process is carried out to pending image based on edge detection operator, the image after rim detection is obtained；

Generation detection sliding window, using progress sliding window detection in image of the detection sliding window after the rim detection；

According to image of the pixel quantity and image subject recognition rule in the detection sliding window after the rim detection It is middle progress image subject extracted region processing, using the image extracted as the pending image main part.

2. the method as described in claim 1, it is characterised in that also include：

The color value of the pixel included in the pending image is obtained, it is pure according to the color value and image of the pixel Color judgment rule determines whether the pending image is solid-color image；

If it is, carrying out edge detection process to the pending image based on the edge detection operator；If it is not, then knot Processing of the beam to the pending image.

3. method as claimed in claim 2, it is characterised in that the pixel included in the acquisition pending image Color value, the color value according to the pixel and image pure color judgment rule determine whether the pending image is pure color Image includes：

Detection boundary is set in the periphery of the pending image, the image detection region in the detection boundary is determined；

The color value of the pixel included in described image detection zone is obtained, pixel of the statistics with same color value is in institute State the accounting in the whole pixels included in image detection region；

The accounting highest color value and accounting value are obtained, judges whether the accounting value is higher than accounting threshold value；

If it is, determining that the pending image is solid-color image.

4. the method as described in claim 1, it is characterised in that the generation detects sliding window, using the detection sliding window in institute Stating progress sliding window detection in the image after rim detection includes：

The edge of image after the rim detection sets horizontal sliding window and vertical sliding window respectively；

The horizontal sliding window, the vertical sliding window are controlled respectively to the central slide of the image after the rim detection, to true Determine the border of described image body region.

5. method as claimed in claim 4, it is characterised in that also include：

The height pixel count of the horizontal sliding window be the rim detection after image width pixel count, the horizontal sliding window Width pixel count is t1；

The width pixel count of the vertical sliding window be the rim detection after image length pixel count, the vertical sliding window Height pixel count is t2；

Wherein, the horizontal sliding window, the vertical sliding window are controlled respectively with step-length t1 and t1 to the image after the rim detection Central slide.

6. method as claimed in claim 4, it is characterised in that also include：

When the edge of image after the rim detection sets the horizontal sliding window, the vertical sliding window, in the level Interference region is removed in sliding window, the vertical sliding window, wherein, the interference region includes：Trade mark region.

7. method as claimed in claim 5, it is characterised in that the pixel quantity according in the detection sliding window and The processing of image subject extracted region is carried out in image of the image subject recognition rule after the rim detection to be included：

The horizontal sliding window, central slide from the vertical sliding window to the image after the rim detection during, respectively The horizontal sliding window, the pixel count included in the vertical sliding window is obtained to slide with the level in a upper sliding position The difference d1 and d2 of pixel count included in window, the vertical sliding window；

Two d1 continuously acquired difference dk1 is calculated, when it is determined that dk1 is more than the first pixel threshold, then stops the level Sliding window is slided and determines the vertical boundary of described image body region；

Two d2 continuously acquired difference dk2 is calculated, when it is determined that dk2 is more than the second pixel threshold, is then stopped described vertical Sliding window is slided and determines the horizontal boundary of described image body region.

8. the method as described in claim 1, it is characterised in that also include：

Before edge detection process is carried out to the pending image based on the edge detection operator, using smoothing filter To being filtered processing to the pending image；

Wherein, the edge detection operator includes：Canny operators.

9. a kind of image subject extraction element, it is characterised in that including：

Edge detection module, for carrying out edge detection process to pending image based on edge detection operator, obtains edge inspection Image after survey；

Sliding window detection module, for generating detection sliding window, using entering in the image of the detection sliding window after the rim detection Row sliding window is detected；

Main body extraction module, for according to it is described detection sliding window in pixel quantity and image subject recognition rule described Image subject extracted region processing is carried out in image after rim detection, the image extracted is regard as the pending image Main part.

10. device as claimed in claim 9, it is characterised in that

Pure color determining module, the color value for obtaining the pixel included in the pending image, according to the pixel Color value and image pure color judgment rule determine whether the pending image is solid-color image；

If the pure color determining module determines that the pending image is solid-color image, the edge detection module is based on institute State edge detection operator and edge detection process is carried out to the pending image；If the pure color determining module is treated described in determining It is not solid-color image to handle image, then terminates the processing to the pending image.

11. device as claimed in claim 10, it is characterised in that

The pure color determining module, is additionally operable to set detection boundary in the periphery of the pending image, determines detection circle Image detection region in limit；The color value of the pixel included in described image detection zone is obtained, statistics has identical face Accounting in whole pixels that the pixel of colour is included in described image detection zone；Obtain the accounting highest face Colour and accounting value, judge whether the accounting value is higher than accounting threshold value；If it is, determining that the pending image is pure Color image.

12. device as claimed in claim 9, it is characterised in that

The horizontal sliding window of setting and hang down respectively the sliding window detection module, the edge of the image being additionally operable to after the rim detection Straight sliding window, controls the horizontal sliding window, the vertical sliding window respectively to the central slide of the image after the rim detection, is used to Determine the border of described image body region.

13. device as claimed in claim 12, it is characterised in that the height pixel count of the horizontal sliding window is examined for the edge The width pixel count of image after survey, the width pixel count of the horizontal sliding window is t1；The width pixel count of the vertical sliding window For the length pixel count of the image after the rim detection, the height pixel count of the vertical sliding window is t2；

The sliding window detection module, is additionally operable to the control horizontal sliding window, the vertical sliding window respectively with step-length t1 and t1 to institute State the central slide of the image after rim detection.

14. device as claimed in claim 12, it is characterised in that

The sliding window detection module, the edge for the image being additionally operable to after the rim detection sets the horizontal sliding window, institute When stating vertical sliding window, interference region is removed in the horizontal sliding window, the vertical sliding window, wherein, the interference region includes： Trade mark region.

15. device as claimed in claim 13, it is characterised in that

The main body extraction module, is additionally operable in the horizontal sliding window, the vertical sliding window to the image after the rim detection Central slide during, obtain respectively the horizontal sliding window, the pixel count included in the vertical sliding window with upper one The difference d1 and d2 of pixel count included in the horizontal sliding window of individual sliding position, the vertical sliding window；Calculating is continuously obtained Two d1 taken difference dk1, when it is determined that dk1 is more than the first pixel threshold, then stops the horizontal sliding window and slides and determine The vertical boundary of described image body region；Two d2 continuously acquired difference dk2 is calculated, when it is determined that dk2 is more than the second picture During plain threshold value, then stop the vertical sliding window and slide and determine the horizontal boundary of described image body region.

16. device as claimed in claim 9, it is characterised in that also include：

The edge detection module, for being carried out based on the edge detection operator to the pending image at rim detection Before reason, using smoothing filter to being filtered processing to the pending image；

Wherein, the edge detection operator includes：Canny operators.

17. a kind of image subject extraction element, it is characterised in that including：

Memory；And

The processor of the memory is coupled to, the processor is configured as based on the instruction being stored in the memory, Perform the image subject extracting method as any one of claim 1 to 8.

18. a kind of computer-readable recording medium, it is characterised in that the computer-readable recording medium storage has computer to refer to The image subject extracting method as any one of claim 1 to 8 is realized in order, the instruction when being executed by processor.