CN102819583B - Network animation method for quickly retrieving - Google Patents

Abstract

The invention discloses network animation method for quickly retrieving, the method effectively utilizes architectural feature and the content characteristic of network animation, realizing quick-searching, it comprises the following steps: (1), based on network animation analytical technology, extracts the architectural feature of network animation to be retrieved；(2) network animated construction feature to be retrieved being compared with the architectural feature of storage in storehouse, if finding same characteristic features, then differentiating that the two is identical network animation；(3) network animation is decomposed into some images, utilizes the method combined based on color entropy and color histogram to extract network animated content feature；(4) comparing cell animated content feature, calculates similarity, if similarity is more than certain threshold value, then differentiates that the two is similar network animation.It is demonstrated experimentally that the present invention can realize the retrieval for network animation accurately and efficiently.

Description

Network animation method for quickly retrieving

Technical field

The present invention relates to network animation based on content retrieval, web advertisement monitoring field, particularly relate to one Network animation method for quickly retrieving.

Background technology

Network animation be a kind of based on vector graphics, there is interactive function and supported by numerous software platforms Multimedia form, has been widely applied to video display, the web advertisement, music MTV, cartoon, game, class The field such as part and cell phone multimedia message.Particularly in terms of the web advertisement, the overwhelming majority of each big main stream website is important Advertisement is all using network animation as carrier.Flourish, in network animation along with network animation The demand hold retrieval, monitoring is the most growing.

Summary of the invention

For in prior art, the shortcomings such as network animation retrieval rate is slow, retrieval is inconvenient, the present invention proposes A kind of method for quickly retrieving that network animated construction feature is combined with content characteristic.

In order to solve above-mentioned technical problem, technical scheme is as follows:

Network animation method for quickly retrieving, comprises the steps:

11) architectural feature of network animation is extracted；

12) calculate the color entropy of network animation and color histogram thus obtain network animated content feature；

13) according to the content characteristic of network animation, the similarity between network animation is calculated, according to the value of similarity Judge that network animation is the most similar.

Further, comprise the steps: that described step 11) comprises the steps:

21) the frame number frame_num in network animation, shape number shape_num, other number of tags are extracted Other_num, by the architectural feature of 3 network consisting animations:

flashStructFeature {

int frame_num;

int shape_num;

int other_num;}；

22) utilize Hash lookup algorithm that network animated construction feature existing in storehouse is made a look up, if searching successfully, Then think that the two is just the same, the network animation information that in storehouse, the match is successful is exported to similar network animation collection Close.

Further, comprise the steps: that described step 12) comprises the steps:

31) network animation is decomposed into some shape image, calculates the color entropy of each shape image, get colors The m pair shape image that entropy is maximum more than certain threshold value and color entropy, using this m sub-picture as this network animation Characteristic image；

Described color entropy computing formula:

$E=-\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{P}_i{\log }_2\left(P_i\right)$

Wherein, P_iRepresent that color i in the percentage ratio shared by entire image, i.e. image of the pixel with color i occurs Probability, m represents the number of the color being quantized in image；

32) described m pair characteristic image is transformed into hsv color space from RGB color, then to HSV Three passage H of color space, S, V uniform quantization respectively is 18,3,3 intervals, the face altogether quantified Chromatic number amount is 18*3*3=162 kind, is calculated the color histogram of m pair characteristic image, and is normalized Process, obtain the content characteristic of network animation.

Further, comprise the steps: that described step 13) comprises the steps:

41) content characteristic that the color entropy in network animation to be compared approximates is selected；

42) histogram intersection method is utilized to calculate the distance between content characteristic；

Two common factor distances d between rectangular histogram f and g (f, g) is calculated as follows:

$d(f,g)=1-\underset{H}{\mathrm{\Σ}}\underset{S}{\mathrm{\Σ}}\underset{V}{\mathrm{\Σ}}\min (f(h,s,v),g(h,s,v))$

Wherein, H, S, V represent that color histogram is in H passage, the quantization district of channel S and V passage respectively Between, described f and g represents the color histogram of two width images of color entropy approximation in network animation respectively； 43) if (f, g) less than certain threshold value, then it is assumed that network animation is similar for distance d between content characteristic. The present invention has the beneficial effects that: effectively utilize architectural feature and the content characteristic of network animation, it is achieved quickly examine Rope, it comprises the following steps: (1), based on network animation analytical technology, extracts network animation to be retrieved Architectural feature；(2) network animated construction feature to be retrieved is compared with the architectural feature of storage in storehouse, If discovery same characteristic features, then differentiate that the two is identical network animation；(3) network animation is decomposed into some Image, utilizes the method combined based on color entropy and color histogram to extract network animated content feature；(4) Comparing cell animated content feature, calculates similarity, if similarity is more than certain threshold value, then differentiates that the two is Similar network animation.It is demonstrated experimentally that the present invention can realize the inspection for network animation accurately and efficiently Rope.The technical field of the application of the present invention includes the necks such as the retrieval of network animated content, network animated content monitoring Territory.

Accompanying drawing explanation

Fig. 1 is the network animation retrieval flow figure of the present invention；

Fig. 2 is Content Feature Extraction and the comparative approach flow chart of the network animation of the present invention.

Detailed description of the invention

Below in conjunction with the drawings and specific embodiments, the present invention is described further.

As Fig. 1 illustrates the network animation method for quickly retrieving flow chart that the present invention proposes, first the method is analyzed Network animation internal structure to be retrieved, extracts its architectural feature, and utilizes Hash lookup algorithm to existing in storehouse Network animated construction feature make a look up, if searching successfully, then it is assumed that the two is just the same, by storehouse Joining successful network animation information to export to similar network animation collections, this step can achieve quick-searching, Large quantities of unmatched network animation can be filtered out, but this step is prioritization scheme, it should be noted that Even if not carrying out this step, the present invention also can be realized.Next the content characteristic of network animation to be retrieved is extracted, And color combining entropy and color histogram map distance calculate this content characteristic and have network animated content feature in storehouse Similarity degree, by similarity degree more than certain threshold value network animation information export to similar network animation collection Closing, return similar network animation collections, to user, is finally completed the work of network animation quick-searching.

The present invention mainly has following two parts: one is the architectural feature extraction of network animation；Two is that network moves Draw Content Feature Extraction and compare.

Two parts of the present invention will be described in detail respectively below.

1. the architectural feature of network animation is extracted

The overall structure of network animation file is by file header, document body and end of file label three part group Become.Wherein, document body is made up of some object tag, and each object tag is too by label Head, label body and label end mark composition.Through analyzing and experimental verification, the frame tagging in network animation Number, shape number of tags and other number of tags combine can be as differentiating that network animation is whether as Important evidence.Therefore, the present invention extracts the frame number frame_num in network animation, shape number shape_num, Other number of tags other_num, by the architectural feature of 3 network consisting animations:

flashStructFeature {

int frame_num;

int shape_num;

int other_num;}

2. the Content Feature Extraction of network animation and Similarity measures

The present invention proposes as shown in Figure 2 the Content Feature Extraction of network animation and comparative approach flow chart.One Individual complete network animation file can resolve from time domain and two aspects of spatial domain.Network animation literary composition Part basic composition unit in time domain is frame, and the basic composition unit in spatial domain is then object mark Sign.The analysis found that network animation has a kind of important object tag: shape (Shape), it is group Become the important basic element of network animation, and save the most of important informations in network animation.Generally In the case of, about a ten seconds network animation file at least has frame up to a hundred, the most only comprises about 20 Shape.Therefore, network animation is decomposed into shape, and the content characteristic of extraction network animation from shape, Recall precision can be significantly increased.

For further speeding up retrieval, available color entropy finds out the most representational shape image in network animation. Color entropy represents the quantity of information of color in image, and color entropy is the biggest, and the colouring information amount in image is the biggest, The image contained much information more can effectively reflect the feature of network animation.

Based on above-mentioned, first network animation is decomposed into some shape image by the present invention, calculates each shape graph The color entropy of picture, the m pair that the entropy that gets colors is maximum more than certain threshold value (general value is 3) and color entropy Shape image, using this m sub-picture as the characteristic image of this network animation.

Color entropy computing formula:

$E=-\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{P}_i{\log }_2\left(P_i\right)$

Wherein, P_iRepresent pixel color i in the percentage ratio shared by entire image, i.e. image with color i The probability occurred, m represents the number of the color being quantized in image.

Then, m pair characteristic image is transformed into hsv color space from RGB color, then to HSV Color space carries out uniform color quantization, is quantized into 162 kinds of colors.Specifically, hsv color space Three passage H, S, V uniform quantization respectively is 18,3,3 intervals, the number of colors the most altogether quantified For 18*3*3=162 kind.It is calculated the color histogram of m pair characteristic image, and is normalized, Content characteristic as network animation；It follows that select between the color entropy in network animation to be compared poor Away from the content characteristic less than threshold value (general value 0.8), histogram intersection distance is utilized to calculate therebetween Distance；

If (h, s, v) (h, s v) represent the color histogram of two width images, then two rectangular histograms f and g to f respectively with g Between common factor distance d (f, g) is defined as follows:

$d(f,g)=1-\underset{H}{\mathrm{\Σ}}\underset{S}{\mathrm{\Σ}}\underset{V}{\mathrm{\Σ}}\min (f(h,s,v),g(h,s,v))$

Wherein, H, S, V represent that color histogram is in H passage, the quantization district of channel S and V passage respectively Between.

Finally, if (f g) less than certain threshold value (general value 0.5), then recognizes distance d between content characteristic It is similar for network animation.

The above is only the preferred embodiment of the present invention, it is noted that common for the art Technical staff, without departing from the inventive concept of the premise, it is also possible to make some improvements and modifications, these Improvements and modifications also should be regarded as in scope.

Claims (5)

1. network animation method for quickly retrieving, it is characterised in that comprise the steps:

11) architectural feature of network animation file is extracted, including time domain characteristic and space domain characteristic；

12) network animation being decomposed into some shape image, calculate the color entropy of each shape image, the entropy that gets colors is more than certain threshold Value and the maximum m pair shape image of color entropy, using this m sub-picture as the characteristic image of this network animation, described m is secondary Characteristic image is transformed into hsv color space from RGB color, is calculated the color histogram of m pair characteristic image, and It is normalized, thus obtains network animated content feature；

13) according to the content characteristic of network animation, utilize histogram intersection method to calculate the distance between content characteristic, calculate network animation Between similarity, judge that network animation is the most similar according to the value of similarity.

Network animation method for quickly retrieving the most according to claim 1, it is characterised in that comprise the steps: described step 11) Comprise the steps:

21) extracting frame number frame_num, the shape number shape_num in network animation, other number of tags other_num, by 3 The architectural feature of network consisting animation:

flashStructFeature{

int frame_num；

int shape_num；

int other_num；}；

22) Hash lookup algorithm is utilized to make a look up, network animated construction feature existing in storehouse if searching successfully, then it is assumed that the two Just the same, the network animation information that in storehouse, the match is successful is exported to similar network animation collections.

Network animation method for quickly retrieving the most according to claim 1, it is characterised in that

Described color entropy computing formula:

$E=-\underset{i=1}{\overset{m}{\Σ}}{P}_i{\log }_2\left(P_i\right)$

Wherein, P_iRepresent the probability that color i occurs in the percentage ratio shared by entire image, i.e. image of the pixel with color i, m table The number of the color being quantized in diagram picture.

Network animation method for quickly retrieving the most according to claim 1, it is characterised in that described step 12) comprise the steps: Three passage H to hsv color space, S, V uniform quantization respectively is 18,3,3 intervals, the number of colours altogether quantified Amount is 18*3*3=162 kind, is calculated the color histogram of m pair characteristic image.

Network animation method for quickly retrieving the most according to claim 1, it is characterised in that comprise the steps:

Described step 13) comprise the steps:

41) content characteristic that the color entropy in network animation to be compared approximates is selected；

42) histogram intersection method is utilized to calculate the distance between content characteristic；

Two common factor distances d between rectangular histogram f and g (f, g) is calculated as follows:

$d(f,g)=1-\underset{H}{\Σ}\underset{S}{\Σ}\underset{V}{\Σ}min\left(f\right(h,s,v),g(h,s,v\left)\right)$

Wherein, H, S, V represent that color histogram divides at H passage, channel S and the quantized interval of V passage, described f and g respectively Biao Shi the color histogram of two width images of color entropy approximation in network animation；

43) if (f, g) less than certain threshold value, then it is assumed that network animation is similar for distance d between content characteristic.