(Luận văn thạc sĩ) trích xuất ảnh trademark dựa trên các đặc trưng bất biến dịch chuyển, quay, tỷ lệ

UNIVERSITY OF ENGINEERING AND TECHNOLOGY VIETNAM NATIONAL UNIVERSITY, HANOI NGUYEN TIENDUNG TRADEMARK IMAGE RETRIEVAL BASED ON SCALE, ROTATION, TRANSLATION, INVARIANT FEATURES MASTER THESIS:INFORMATION TECHNOLOGY Hanoi - 2014 UNIVERSITY OF ENGINEERING AND TECHNOLOGY VIETNAM NATIONAL UNIVERSITY, HANOI NGUYEN TIENDUNG TRADEMARK IMAGE RETRIEVAL BASED ON SCALE, ROTATION, TRANSLATION, INVARIANT FEATURES Major : Computer Science Code : 60480101 MASTER THESIS: INFORMATION TECHNOLOGY Supervised by: Dr Le Thanh Ha Hanoi - 2014 Hanoi - 2010 Originality Statement ‘I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at University of Engineering and Technology (UET) or any other educational institution, except where due acknowledgement is made in the thesis I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project’s design and conception or in style, presentation and linguistic expression is acknowledged.’ Signed TABLE OF CONTENS Originality Statement ABBREVIATION Abstract Chapter 1: Introduction Chapter 2: Related work 11 Chapter 3: Background 14 3.1 Pre-processing 14 3.2 Object description 19 3.3 Feature vectors extraction 20 3.3.1 Discrete Fourier Transform (DFT) 20 3.3.2 Log-polar transform 21 3.4 Measure of similarity 22 3.4.1 Euclidean distance 22 3.4.2 Mahalanobis distance 23 3.4.3 Chord distance 23 Chapter 4: Proposed method 24 4.1 Pre-processing 24 4.2 Visual shape objects extraction 24 4.3 Scale, rotation, translation invariant features 25 4.4 Measure of similarity 28 Chapter 5: Experiments and results 30 5.1 Implementation 30 5.2 Test results for exact copy actions 32 5.3 Test results for scaling action 33 5.4 Test results for rotating actions 34 5.5 Test results for mirror actions 35 5.6 Test results for partial copy actions 36 5.7 Test results for random query trademark 38 5.8 Testing summary 38 Chapter 6: Conclusion 40 REFERENCES 41 APPENDIX 45 Pre-processing 45 Visual shape objects extraction 45 Scale, rotation, translation invariant features extraction 47 Matching by measure of similarity and retrieval Trademark Images 49 List of Figure Fig Some trademark image samples Fig The log-polar transform maps (𝐱, 𝐲) into (𝐥𝐨𝐠(𝐫), 𝛉) 21 Fig Log-polar transform of rotated and scaled squares: size goes to a shift on the 𝐥𝐨𝐠(𝐫) axis and rotation to a shift on the 𝛉 − 𝐚𝐱𝐢𝐬 22 Fig Contour filter algorithm 25 Fig Illustration of three stages of the proposed method 28 Fig Samples of the collected trademark images for testing 30 Fig Results for exact copy tests 32 Fig Result for scaling tests 33 Fig Results for translation and scaling tests 34 Fig 10 Results for rotation tests 35 Fig 11 Results for mirror tests 36 Fig 12 Results for parital copy tests 37 Fig 13 Results for random tests 38 ABBREVIATION DFT: Discrete Fourier Transform CBIR: Content Based Image Retrieval SIFT: Scale-invariant feature transform Abstract Trademark registration offices or authorities have been bombarded with requests from enterprises These authorities face a great deal of difficulty in protecting enterprises’ rights such as copyright, license, or uniqueness of logo or trademark since they have only conventional clustering Urgent and essential need for sufficient automatic trademark image retrieval system, therefore, is entirely worth thorough research In this thesis, a novel trademark image retrieval method is proposed in which the input trademark image is first separated into dominant visual shape images then a feature vector for each shape image which is scale-, rotation-, and translation- invariant is created Finally, a similarity measure between two trademark is calculated based on these feature vectors Given a query trademark image, retrieval procedure is carried out by taking the most five similar trademark images in a predefined trademark Various experiments are conducted to mimic the many types of trademark copying actions and the experimental results exhibit the robustness of our retrieval method under these trademark copy actions Chapter 1: Introduction From an economic perspective, a trademark is clearly understood as a word, a design, a picture, a complex symbol or even a combination of such, which is put on a product or standing for service of particular company In [2], four types of popular trademarks are listed in order of visual complexity: word-in-mark (only characters or words in the mark), device-mark (graphical or figurative elements), composite-mark (characters or words and graphical elements) and complex-mark (complex image) Fig offers some trademark samples Fig Some trademark image samples Every Company or Financial organization desires to own a distinctive, meaningful, and descriptive logo which offers both exclusive and right of its characteristic Drawing attention of consumers to their products or services and market viability dependsactually on not only designing an intellectual and attractive trademark, but also whether or not preventing consumer confusion The world markets have remarkably expandedand grown for global economic scenario caused by different trade related practices coming closer to each other at international level A great number of businesses have been established This has resulted in millions of trademarks submitted to various trademark offices world over for registration need to have distinctiveness from the existing trademarks as per definitions and trade practices in different countries and this is likely to be on an increase in years to come.Actually, millions of trademarks already registered and millions of applications filed for trademarks registration are aggravating the problem of issuing the trademark certificates Therefore, the trademark registration authorities have received many trademark protection applications from enterprises.The problem for finding the similar trademark has become a challenge because These authorities face challenges in dealing with these proposals since they still use the traditional activity of classification (i.e., manual way) It is obvious that trademark registration with manual searching is very arduous task for the officials.It is really hard for them to make sure if a trademark is duplicated: whether a particular trademark is registered or not; if a trademark resembles another registered trademark in any way, or, if copyright or license of trademark is infringed Thus, this poses an urgent need for an alternative automatic technology In [33], there are different techniques and approaches currently in use for distinctness check for trademarks.The most popular and appreciated image processing techniques and approaches for the trademark distinctness check are Content Based Image retrieval techniques, which widely used for that purpose and some other approaches like shape and texture based similarity finding techniques are also used.Image processing tools and techniques can be used to solve different problems related to image, text, graphics and color etc A Trademark can be a combination of text, graphics, image, and colored texture Based on these, one can divide them in these components for finding the similarity among different trademarks retrieved from the trademark database Most of the recent techniques used for the image retrieval have mainly utilized the features like color, texture, shape etc They used existing CBIR technique, i.e Content Based Image Retrieval Systems to retrieve the images based on visual features like texture, color, shape In this technique extraction of color feature using the color histogram technique is utilized It also considers the shape feature because it is an important feature in CBIR applications Many techniques or approaches have been utilized for the image retrieval, some of which are based on improved pattern matching algorithms Some others take a much broader approach like searching just from the text files Some are based on shape and color feature and some have attempted morphological pattern based image matching and retrieval using a database A shape based technique introduced for the logo retrieval reported in a paper is also inadequate to solve the problem amicably In this thesis, a novel trademark image retrieval method is proposed in which the input trademark image is first separated into dominant visual shape images then a feature vector for each shape image which is scale-, rotation-, and translation- invariant is created Finally, a similarity measure between two trademark is calculated based on these feature vectors The manuscript entitled “Trademark Image Retrieval Based on Scale, Rotation, Translation, Invariant Features” related the issue of my thesis was published inComputing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2013 IEEE RIVF International Conference on10-13 Nov 2013 tmp.copyTo(q3); q1.copyTo(tmp); q2.copyTo(q1); tmp.copyTo(q2); normalize(dst, dst, 0, 1, CV_MINMAX); } void POLARTRANSFORM(Mat &src,Mat&dst) { CvMat *src1=&src.operatorCvMat(); CvMat *dst1=cvCreateMat(360,425,CV_32FC1); cvLogPolar( src1, dst1, cvPoint2D32f(src1>cols/2,src1->rows/2), 78,CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS); dst=cvarrToMat(dst1,true); } void RBRC(Mat &src,Mat&dst) { Mat src1; DFT(src,src1); POLARTRANSFORM(src1,dst); DFT(dst,dst); } Matching by measure of similarity and retrieval Trademark Images Description In order to recognize the copied trademark image, we derive a trademark similarity measure based on its feature vectors Input A trademark input image Output Most five similar trademark images After stage of creating feature vectors, a trademark input image is Idea represented by one or two feature vector Let 𝐼 and 𝐼′ the two trademark image, we suppose that 𝐹𝑖 and 𝐹𝑗 𝑤𝑕𝑒𝑟𝑒 𝑖 = 1, 2; 𝑗 = 1, are feature 49 vectors of 𝐼and 𝐼′ , respectively.We propose that degree of similarity of two trademarks 𝐼, 𝐼′ signed 𝑆(𝐼, 𝐼′ ) is primarily the smallest distance between two feature vectors; one in set (𝐹𝑖 )and one in set (𝐹𝑗 ) denoted by 𝑑𝑖𝑠𝑡(𝐹𝑖 , 𝐹𝑗 ) We employed Euclidian distance to compute the distance of two feature vectors, which can be expressed as follows: 𝑆 𝐼, 𝐼′ = min⁡ {dist 𝐹𝑖 , 𝐹𝑗 }𝑤𝑕𝑒𝑟𝑒 𝑖, 𝑗 = 1,2 Source code void VECTORDISTANCE(vectorfeaturevectors,Mat&padded,vect or&distance) { double a; Mat dst,sub1; RBRC(padded,dst); for(size_t i=0;i