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 0 TIEU LUAN MOI download : skknchat@gmail.com 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 1 Hanoi - 2010 TIEU LUAN MOI download : skknchat@gmail.com 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. 2 TIEU LUAN MOI download : skknchat@gmail.com TABLE OF CONTENS Originality Statement.
8 Chapter 2: Related work. Feature vectors extraction. Discrete Fourier Transform (DFT). Log-polar transform.
Measure of similarity. 23 Chapter 4: Proposed method. Visual shape objects extraction. Scale, rotation, translation invariant features.
Measure of similarity. 28 Chapter 5: Experiments and results. Test results for exact copy actions. 32 3 TIEU LUAN MOI download : skknchat@gmail.
Test results for scaling action. Test results for rotating actions. Test results for mirror actions. Test results for partial copy actions.
Test results for random query trademark. 45 Visual shape objects extraction. 45 Scale, rotation, translation invariant features extraction. 47 Matching by measure of similarity and retrieval Trademark Images.
49 4 TIEU LUAN MOI download : skknchat@gmail.le List of Figure Fig. Some trademark image samples. The log-polar transform maps (𝐱, 𝐲) into (𝐥𝐨𝐠(𝐫), 𝛉). 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.
Contour filter algorithm. Illustration of three stages of the proposed method. Samples of the collected trademark images for testing. Results for exact copy tests.
Result for scaling tests. Results for translation and scaling tests. Results for rotation tests. Results for mirror tests.
Results for parital copy tests. Results for random tests. 38 5 TIEU LUAN MOI download : skknchat@gmail.le ABBREVIATION DFT: Discrete Fourier Transform CBIR: Content Based Image Retrieval SIFT: Scale-invariant feature transform 6 TIEU LUAN MOI download : skknchat@gmail.le 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. 7 TIEU LUAN MOI download : skknchat@gmail.le 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. 1 offers some trademark samples. 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. 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 8 TIEU LUAN MOI download : skknchat@gmail.le 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. 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 9 TIEU LUAN MOI download : skknchat@gmail.le My thesisis organized as follows: Chapter 1 is introduction of my thesis.
Chapter 2 represents some related works. Chapter 3 illustrates background about some problems related. Chapter 4 presents proposed method in detail. Chapter 5 provides installation of Visual Studio 2010 with OpenCV2.2 on Window 7 for implementing my thesis anda presentation of experimental results.
Chapter 6 is conclusionof the thesis. Additionally, in Appendix, I show the whole of source code of my thesis for reading convenience. 10 TIEU LUAN MOI download : skknchat@gmail.le Chapter 2: Related work In recent years, researchers have proposed a wide range of solutions in a bid to alleviate the workload for the trademark registration offices. Chen, Sun, Yang [1], suggested two main steps for computing features vector.
Initially, object region extracted from a principal orientation-rotated image is equally partitioned into 16 regions. Then an entropy vector as feature vector of an image is constructed by computing information entropy of each partitioned region. This automatic shape-based retrieval technique achieves the desired performance, good invariance under rotation, translation, scale, noise, degree of thickness, and human visual perception satisfaction. However, this single-feature retrieval system does not seem to meet multi-aspect of appreciation.
To improve this, among others, single-feature Zernike Moments (ZM) in [4, 10] and invariant moments in [3, 5, 6] features of each are combined with other features.Experiment results presented by [4] showed that this method has steady performance and good invariant property under translation, rotation and scale. Moreover, and the low noise sensitivity of Zernike moments made this method more insensitive to noise. However, because different users have different understanding for image similarity, the present methods of trademark image retrieval have some shortcomings in some aspects such as the retrieval ability to geometric deformation images, retrieval accuracy, the consistency between image and human visual perception. Yet the retrieval by using Zernike moment method in [10]shows it can rapidlyretrieve trademarks.
A new method is proposed in [3] based on cosine distance and normalized distance measures. The cosine distance metric normalizes all features vector to unit length and makes it invariant against relative in-plane scaling transformation of the image content. The normalized distance combines two distances measures such as cosine distance and Euclidean distance which shows more accuracy than one method alone. The proposed measures take into account the integration of global features (invariant moments and eccentricity) and local features (entropy histogram and distance histogram).
It first indexes trademark images database (DB) in order to search for trademarks in narrowed limit and reduce time computation and then calculates similarities for features vector to obtain the total similarity between features vector.An alternative solution worth a mentioning is four shape features: global features (invariant moments and eccentricity) and local features (entropy histogram and distance histogram) [16] are exploited by [3]. 11 TIEU LUAN MOI download : skknchat@gmail.le Recently, [5] combined nine feature vectors of low-order color moments in HSV color space with low-orders Hu moments and eccentricity which are extracted from gray shape- region image by Rui and Huang’s (1998) technique. The way of Gauss normalization is applied to those features, and the weight of every feature can be adjusted flexibly [17].Good results have been obtained in the experiments which prove that the multi-feature combination way is better than other single-feature ways. These features are input to an ensemble of RBFNNs trained via a minimization of the localized generalization error to recognize the trademark images.
In this current system, the trademark images are black- and-white images. The system will be improved to adopt color trademark images as further study. In [2, 7], the ways of proposed combination of features are definitely different. It is admitted that each of them performs well.
The equidistance based on concentric circles [14] is used to partition region, labelled the first step in [4] and [2]. Iwanaga, et al [7] put forward the modified angle-distance pair-wise histogram based on the angle histogram and distance histogram of trademark object. This system outperforms both moment-based and independent histogram; i. angle, distance, color.