CN100363943C - Color Image Matching Analysis Method Based on Color Content and Distribution - Google Patents
Color Image Matching Analysis Method Based on Color Content and Distribution Download PDFInfo
- Publication number
- CN100363943C CN100363943C CNB2004100197213A CN200410019721A CN100363943C CN 100363943 C CN100363943 C CN 100363943C CN B2004100197213 A CNB2004100197213 A CN B2004100197213A CN 200410019721 A CN200410019721 A CN 200410019721A CN 100363943 C CN100363943 C CN 100363943C
- Authority
- CN
- China
- Prior art keywords
- image
- color
- matching
- matrix
- images
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 31
- 239000011159 matrix material Substances 0.000 claims abstract description 24
- 238000000034 method Methods 0.000 claims abstract description 20
- 230000000877 morphologic effect Effects 0.000 claims abstract description 17
- 239000013598 vector Substances 0.000 claims description 4
- 238000013139 quantization Methods 0.000 claims description 2
- 238000005516 engineering process Methods 0.000 abstract description 14
- 238000001914 filtration Methods 0.000 description 10
- 238000004364 calculation method Methods 0.000 description 5
- 239000003086 colorant Substances 0.000 description 5
- 230000000694 effects Effects 0.000 description 5
- 238000010191 image analysis Methods 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 239000000284 extract Substances 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 101150012579 ADSL gene Proteins 0.000 description 1
- 102100020775 Adenylosuccinate lyase Human genes 0.000 description 1
- 108700040193 Adenylosuccinate lyases Proteins 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000003703 image analysis method Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012552 review Methods 0.000 description 1
Images
Landscapes
- Image Analysis (AREA)
Abstract
本发明涉及基于色彩内容及分布的彩色图像匹配分析方法。本发明采用基于彩色直方图的模糊相关与形态矩阵分析相结合的方法，将输入图像与数据库(具有一定特征的图像的集合)中的参考图像进行匹配分析，以判定是否在参考图像数据库中存在与输入图像相匹配的图像。包括下述步骤：提取输入图像和参考图像的彩色直方图，根据两直方图的匹配程度判定两图像匹配与否；其次提取输入图像特定区域的形态特征，并得到其形态矩阵，根据其与参考图像的形态矩阵比较的结果，判定其匹配程度。本发明在不良图像的识别与分析应用中，较现有的相应技术有高效率、低误判率与高准确率等优点。
The invention relates to a color image matching analysis method based on color content and distribution. The present invention adopts the method of combining the fuzzy correlation based on the color histogram and the morphological matrix analysis, and performs matching analysis on the input image and the reference image in the database (a collection of images with certain characteristics), so as to determine whether the reference image exists in the reference image database. An image that matches the input image. It includes the following steps: extracting the color histogram of the input image and the reference image, judging whether the two images match or not according to the matching degree of the two histograms; secondly extracting the morphological features of the specific area of the input image, and obtaining its morphological matrix, according to its The results of the image's morphological matrix comparison are used to determine the degree of matching. Compared with the existing corresponding technology, the present invention has the advantages of high efficiency, low misjudgment rate and high accuracy rate in the application of identification and analysis of bad images.
Description
Technical Field
The invention belongs to an image analysis technology, in particular to a color image matching analysis method based on color content and distribution.
Background
With the increase of network speed and the increase of the number of people on the internet, the internet becomes a global economic and cultural communication link, and meanwhile, brings opportunities for lawbreakers, and the problem that bad information such as pornographic images and the like is spread on the internet is caused. The identification and analysis of poor (e.g., pornographic) images has become a hot problem in the current development of the internet.
How to analyze, identify and filter the bad images is a research topic which is widely focused on nowadays. The traditional filtering technology based on keywords is gradually replaced by the analysis technology based on image content due to the defects of low accuracy rate, multiple meanings and the like. The analysis based on the image content is to extract some features representing the content of the input image, such as color, shape, texture, structure and the like, when the image is analyzed, and then carry out matching analysis on the features and the corresponding features of the reference image, so as to realize the identification or the filtration of the input image.
In the color image matching analysis technology based on image content, a color histogram (oil k. Jain and adithya variaya 1996 Pattern Recognition 29 1233) is applied to extract the pixel number ratio corresponding to each color in an image, so that the color parameters of the image can be analyzed from a statistical angle, and the color image matching analysis technology has the advantages of scale invariance and rotation invariance.
The existing color image matching analysis technology based on color histogram can be divided into the following two categories:
(1) Techniques for image matching analysis using histograms only (Aigranin O H, zhang H, petkovic D. Content-based presentation and geographic of visual media: a state-of-the-art review. Multimedia Tools and Application,1996,3 (1): 179-182). The technique first extracts the color histogram of the color image. Then, according to a certain rule, the matching judgment is directly carried out on the specific color part of the histogram. The technology is insensitive to the geometric distribution of colors, so that the matching analysis precision is low.
(2) Image matching analysis techniques (Cinque L, ciocca G, levialdi S, et al color-based image retrieval spatial-colorimetric imaging image visual Computing, 2001.19). The technology combines the color histogram with the position of a specific color pixel, and extracts the color statistical characteristic and the position characteristic of the pixel so as to improve the accuracy of image matching judgment. However, the position information extraction of the pixels is simple, and the spatial and logical relationship between the pixels cannot be embodied, so that the analysis effect of the technology is greatly influenced.
In a word, the existing color image matching analysis technology has the disadvantages of unsatisfactory effect, low image matching precision, and high misjudgment rate when applied to image filtering, which affects the actual application effect.
Disclosure of Invention
The object of the present invention is to provide a new color image analysis method based on color content and distribution, which can overcome the disadvantages and shortcomings of the prior art. Compared with the prior art, the method has the advantages of high efficiency, low misjudgment rate, high accuracy and the like in the identification and analysis application of the bad images.
The invention adopts a method of combining fuzzy correlation based on a color histogram with morphological matrix analysis, and carries out matching analysis on an input image and a reference image in a database (a set of images with certain characteristics) so as to judge whether an image matched with the input image exists in the reference image database. The method comprises the following steps:
extracting color histograms of an input image and a reference image, and judging whether the two images are matched or not according to the matching degree of the two histograms; then, the form characteristics of the specific area of the input image are extracted, the form matrix of the input image is obtained, and the matching degree of the input image is judged according to the result of comparing the form matrix of the input image with the form matrix of the reference image.
1) Extracting a color histogram of the image by adopting a color quantization method, and according to a fuzzy relation membership function:
and a matching threshold value alpha 1 And determining the color peak pair of the color matching.
2) According to the following formula and a matching threshold value alpha 2 Determining a highly matched color matching peak pair:
adding them to obtain
And according to a threshold value alpha 3 And judging whether the images are matched.
3) The particular pair of matching color peaks may be weighted and the degree of matching of the image calculated according to the following equation, as desired.
Wherein the weight is u i Representing the degree of emphasis on different pairs of matching color peaks and according to a threshold value alpha 3 It is determined whether the images match.
4) And taking the form matrix as a characteristic parameter, extracting and comparing form information of the image, and calculating whether the form characteristics of the image are matched according to the following formula and a matching threshold value r':
the invention has the positive effects that:
1) High efficiency: the method adopts methods such as color histogram, weighting, fuzzy correlation, form matrix and the like, adopts methods such as mathematical statistics and the like, does not relate to complex digital image processing operations such as boundary analysis, geometric calculation and the like, greatly controls the mathematical complexity of image analysis, and obviously improves the efficiency of image matching analysis.
2) Low misjudgment rate:
because the invention adopts a plurality of image recognition technologies and combines with the color histogram, the invention can extract and analyze a plurality of information of the image more comprehensively and can carry out matching analysis on the image from a plurality of different angles as much as possible. Therefore, when the matching judgment between the input image and the reference image in the sample database is performed, the accuracy of the image matching judgment can be greatly improved.
3) High accuracy:
the invention adopts a weighting algorithm, so that a certain specific color can be artificially and heavily analyzed; because of the adoption of a fuzzy correlation comparison method of the histogram, the method has certain intelligence in the aspect of color matching, has good adaptability to the color discrimination of the image, and improves the actual comparison effect; by adopting the form matrix method, the form distribution information of the image can be comprehensively extracted and analyzed, whether the form characteristics of the input image are matched with the form characteristics of the reference image or not is judged from the form characteristics of the input image, and a better identification effect is achieved.
4) The technology is particularly suitable for identifying and filtering color images.
The color image matching analysis method provided by the invention can filter the color image input by the network according to the matching degree of the color image and the reference image in the poor image library (defined according to the color and the morphological characteristics). Hardware equipment required by a filtering system of network bad images: a hub or a router, a network server, a communication line and poor image filtering system software running at the server end. The data sent by the Internet is analyzed at the server side through the filtering system software, the data containing the bad image information are filtered, then the data are sent to each terminal, and meanwhile, the related website and the access terminal are written into the network service log for query.
Drawings
Fig. 1a is an image S.
Fig. 1b is an image T.
FIG. 1c is a color histogram H of the image S S 。
FIG. 1d is a color histogram H of an image T T 。
Fig. 2 is a schematic diagram of extraction of an image morphology matrix.
FIG. 3: detailed flow chart of the invention.
FIG. 4: the structure of the system is schematic.
Detailed Description
The salient and substantial features and improvements of the present invention can be seen in the examples that follow. They do not limit the invention in any way.
As shown in the figure:
1. matching of image color information
The input image S is compared with the image T in the reference image database, and the matching relationship thereof is determined.
1) Extracting color histograms
Replacing the original RGB colors of the images S and T with 16 index colors, and establishing a color histogram H of the images S and T S And H T (see FIG. 1).
2) Color matching of histogram color peaks
Calculating histogram H one by using fuzzy relation membership function (1) formula of color S And H T Color matching coefficient between all color peaks in (1):
wherein
And
respectively represent histograms H S And H T A pair of color vectors to be compared.
Deblurring according to an alpha-level relationship, having
Namely when
Greater than a certain value alpha 1 When the utility model is used, the water is discharged,
take the value of 1 and consider this color vector
And a known color vectorAnd otherwise, the data are not matched.
Extracting a histogram H according to the matching analysis result S And H T All color matched color pairs in (1) and noted
3) Height matching of color matching peaks
For two histograms H S And H T The color peak of the medium color matching calculates its height matching coefficient according to the following equation (4):
wherein h is i And h i ' indicates the heights of two color matching peaks, respectively. The fuzzy matching relationship between the heights of the two color matching peaks is determined according to the following formula:
namely whenGreater than a certain value alpha 2 When the utility model is used, the water is discharged,
the value is 1, and the color peak pair is considered to be matched, otherwise, the value is notThere is no match.
4) Matching of color histograms
Accumulating the number of the color peak pairs corresponding to the matching obtained by the calculation to obtain fuzzy matching coefficients of the two color histograms:
where m is the total number of color peaks in the color histogram.
The fuzzy match relationship of the two color histograms is determined as follows:
when R is h Greater than a certain value alpha 3 When R is h The value is 1 and the color histogram pair is considered to match, otherwise it is not.
5) Weighted analysis of specific colors
To emphasize the match between particular colors of images S and T, the calculation of equation (6) can be applied to determine the color peak corresponding to the particular colorMultiplying the value by a set value u i To increase the weight of the color peak in the color histogram matching calculation:
to determine the matching degree between color images after color weighting.
2. Analysis of image morphological features
If the input image S and the image T in the reference image database cannot be matched according to the color features, further matching analysis needs to be performed on morphological features of the image S and the reference image T:
1. the image S is color-segmented, and an area of the image with a certain color to be analyzed is extracted and marked as F.
2 divide the region F at regular intervals as shown in fig. 2. Calculating the number of pixels and the area F included in each gridA ratio of the total number of pixels of (1), each grid obtaining a ratio value W ij In W with ij Is an element, a matrix W = { W is calculated ij The i, j is determined by a mesh division method }, called a form matrix, and records form and distribution information of the image S.
And 3, performing matching calculation with data in the sample image morphological characteristic database based on the morphological matrix W of the image S.
Let W' be a morphological matrix in the sample image morphological feature database, then their degree of match can be calculated by:
if the matching threshold is R ', then when R' is less than or equal to R ', the two morphology matrices W and W' are considered to be matched, i.e. the image S has data
And (4) considering the sample morphological information in the library, namely the image S is considered as a matching image.
Fig. 3 is a detailed flow chart of the present invention. The method comprises the following specific steps:
1. the image is input, denoted as A.
2. Histogram information of the image a is extracted to obtain a histogram H (a).
3. Reference is made to the image database histogram dataset H (D).
4. And calculating the matching degree of the histograms H (A) and H (D) according to a weighting method, namely judging whether the image A is matched with each reference image T in the image database.
5. Reference is made to the image database morphology matrix dataset S (D).
6. It is determined whether the degree of matching is greater than a predetermined value. If not, turning to 7; if so, turn to 10.
7. If the judgment of 6 is not, the form matrix S (A) of the image A is extracted, and the matching degree of the S (A) and the S (D) is calculated.
8. And (4) judging whether the matching degree parameter is larger than a preset value or not by the matching degree parameter calculated by the step (7). If not, turning to 9; if so, turn to 10.
9. And outputting an image analysis result, namely the image A is a mismatched image.
10. And outputting an image analysis result, namely the image A is a matching image.
Fig. 4 is a system configuration diagram. The components and functions of the system structure, a hub or a router, a network server, a communication line and the poor image filtering system software running at the server end are described in detail. And analyzing the data sent by the Internet at the server side through filtering system software, filtering the matched data containing the poor image information, then sending the data to each terminal, and simultaneously writing related websites and access terminals into a network service log for query. FIG. 4 illustrates the following in detail:
1. internet access server network server 3 (the access mode can be ADSL or broadband access).
2. And the bad image filtering software analyzes data flowing into the server from the Internet based on the technologies such as fuzzy correlation, weighting and form matrix, blocks data containing suspicious image contents, records visitors of the images and the sources of the images, and writes the data into a database for query. The software runs on a web server 3.
3. The web Server (which may be IBM xSeries 206 or SUN Fire V60x Server) accesses the Internet. (twisted pair or optical fiber may be used for connection to the internet).
4. Network switch (optional D-Link)DES-3226Or 3ComSuperStack3Switch 4400).
5. Connected with a network switch (the connection can adopt twisted pair, coaxial cable or wireless connection and the likeMode) network terminal (IBMThinkCentreM or Dimension can be selected TM 8300 and the like desktop and notebook computers).
Claims (1)
1. A method of color image matching analysis comprising the steps of:
extracting color histograms of the input image and the reference image, judging whether the two images are matched or not according to the matching degree of the two histograms, extracting morphological characteristics of a specific area of the input image, obtaining a morphological matrix of the input image, and judging the matching degree according to a comparison result of the morphological matrix of the input image and the morphological matrix of the reference image; the method is characterized in that:
1) Extracting color histogram of image by color quantization method, and membership function based on fuzzy relation
And a matching threshold value alpha 1 Determining color peak pairs of color matching; wherein
And
a pair of color vectors to be compared respectively representing the histogram input image and the images in the reference image database;
2) According to the following formula and a matching threshold value alpha 2 Determining a highly matched color matching peak pair:
add them up to obtain
And according to a threshold value alpha 3 Determining whether the images match, wherein m is the total number of color peaks in the color histogram;
3) For weighting a particular pair of matching color peaks, R in step 2) h Calculated according to the following formula:
wherein the weight is u i Representing the degree of emphasis on different pairs of matching color peaks and according to a threshold value alpha 3 Judging whether the images are matched;
4) And taking the form matrix as a characteristic parameter, extracting and comparing form information of the image, and calculating whether the form characteristics of the image are matched according to the following formula and a matching threshold r':
w of it ij For inputting a matrix of image morphology, W ij ' is an image morphology matrix in a reference image database.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB2004100197213A CN100363943C (en) | 2004-06-21 | 2004-06-21 | Color Image Matching Analysis Method Based on Color Content and Distribution |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB2004100197213A CN100363943C (en) | 2004-06-21 | 2004-06-21 | Color Image Matching Analysis Method Based on Color Content and Distribution |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN1595434A CN1595434A (en) | 2005-03-16 |
| CN100363943C true CN100363943C (en) | 2008-01-23 |
Family
ID=34663062
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CNB2004100197213A Expired - Fee Related CN100363943C (en) | 2004-06-21 | 2004-06-21 | Color Image Matching Analysis Method Based on Color Content and Distribution |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN100363943C (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN100463002C (en) * | 2006-12-07 | 2009-02-18 | 北京航空航天大学 | Image Matching Method Based on Pixel Skip |
| CN101470730B (en) * | 2007-12-26 | 2010-12-22 | 中国科学院自动化研究所 | Image repetition detection method based on spectrum characteristics analysis |
| CN102542544A (en) * | 2010-12-30 | 2012-07-04 | 北京大学 | Color matching method and system |
| CN103312770B (en) * | 2013-04-19 | 2017-05-03 | 无锡成电科大科技发展有限公司 | Method for auditing resources of cloud platform |
| CN107869747A (en) * | 2017-09-18 | 2018-04-03 | 深圳市盛路物联通讯技术有限公司 | Device management method and related product |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002049642A (en) * | 2000-05-22 | 2002-02-15 | Eastman Kodak Co | Method for analyzing images to determine one or more sets of materials corresponding to analyzed images |
-
2004
- 2004-06-21 CN CNB2004100197213A patent/CN100363943C/en not_active Expired - Fee Related
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002049642A (en) * | 2000-05-22 | 2002-02-15 | Eastman Kodak Co | Method for analyzing images to determine one or more sets of materials corresponding to analyzed images |
Non-Patent Citations (3)
| Title |
|---|
| 基于代表色的图像检索方法研究. 杨关良，李忠杰，徐小杰.仪器仪表学报，第4卷第24期. 2003 * |
| 基于相关的彩色图像检索. 梁艳梅，翟宏琛，母国光.中国科学E辑，第10卷第33期. 2003 * |
| 形态描述矩阵及其在彩色图像检索与识别中的应用. 王熠，翟宏琛，梁艳梅，张思远，母国光.中国科学E辑技术科学，第3卷第34期. 2004 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN1595434A (en) | 2005-03-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102014295B (en) | Network sensitive video detection method | |
| CN100361450C (en) | A system that blocks pornographic images and inappropriate information on the Internet | |
| CN110351244A (en) | A kind of network inbreak detection method and system based on multireel product neural network fusion | |
| CN106682108B (en) | Video retrieval method based on multi-mode convolutional neural network | |
| CN110263666B (en) | Action detection method based on asymmetric multi-stream | |
| CN102880873B (en) | Personnel behavior identification implementation system and method based on image segmentation and semantic extraction | |
| CN107169106B (en) | Video retrieval method, device, storage medium and processor | |
| CN109309675A (en) | A network intrusion detection method based on convolutional neural network | |
| WO2012078702A1 (en) | Video key frame extraction using sparse representation | |
| CN101394522A (en) | Method and system for detecting video copy | |
| CN115131714A (en) | Video image intelligent detection and analysis method and system | |
| CN107465691A (en) | Network attack detection system and detection method based on router log analysis | |
| CN114677333B (en) | A histogram-based image contrast enhancement detection method | |
| CN114049508B (en) | Fraud website identification method and system based on picture clustering and manual research and judgment | |
| CN111507385A (en) | A Scalable Network Attack Behavior Classification Method | |
| CN114332559A (en) | An RGB-D Saliency Object Detection Method Based on Adaptive Cross-modal Fusion Mechanism and Deep Attention Network | |
| CN115118451A (en) | A Network Intrusion Detection Method Combined with Graph Embedding Knowledge Modeling | |
| CN111275694B (en) | A system and method for progressive division of human body parsing guided by attention mechanism | |
| CN116721354A (en) | Building crack defect identification method, system and readable storage medium | |
| CN100363943C (en) | Color Image Matching Analysis Method Based on Color Content and Distribution | |
| CN116069607A (en) | Abnormal Behavior Detection Method of Mobile Office Users Based on Graph Convolutional Neural Network | |
| CN111754459B (en) | Coloring and forgery image detection method and electronic device based on statistical depth features | |
| CN111597423B (en) | Performance evaluation method and device of interpretable method of text classification model | |
| CN110597993A (en) | A Data Mining Method for Microblog Hot Topics | |
| CN110689074A (en) | A Feature Selection Method Based on Fuzzy Set Feature Entropy Calculation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| C19 | Lapse of patent right due to non-payment of the annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |