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Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
Btv thesis defense_v1.02-final
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Btv thesis defense_v1.02-final

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  • 1. Improvement of Content-Based Image Retrieval by Using Clustering and Relevance Feedback Master Thesis Defense Bui The Vinh May 13, 2010
  • 2. Content  Introduction  Image’s Features & Similarity  Clustering Algorithm  Relevance Feedback  Implementation and Evaluation  Conclusions and Future Work 2
  • 3. Introduction 3  Key points  How to represent an image  How to determine whether two images are similar or not  Framework
  • 4. Introduction 4  Practical Applications  Medical diagnosis  Crime prevention  Online shopping  Etc.  Challenges  Real-time system  High accuracy  Contributions  Build a complete CBIR system  Improve the searching time by using clustering  Increase the accuracy by applying support vector machine in Relevance Feedback
  • 5. Content  Introduction  Image Features & Similarity  Clustering Algorithm  Relevance Feedback  Implementation and Evaluation  Conclusions and Future Work 5
  • 6. Feature Extraction Model 6 F1 B F2 F3  Basic Image features: COLOR, SHAPE, TEXTURE
  • 7. Image Representation 7  Image representation  CEDD: Color and edge directivity descriptor (proposed by Chatzichristofis and Boutalis)  Incorporate color and texture information in a histogram  Each image is represented by a high dimensional real vector 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0  Vectors representing images depend on the method of extracting image features
  • 8. Similarity Measurement 8  Formula  Calculate the distance between two corresponding vectors  Tanimoto distance F1 F3 F2
  • 9. Content Introduction Image’s Features Clustering Algorithm Relevance Feedback Implementation and Evaluation Conclusions and Future Work 9
  • 10. Overview of Clustering 10  Motivation  The amount of image data involved is very large  Finding groups of objects such that:  The objects in a group will be similar to one another  The objects in a group will be different from the objects in other groups
  • 11. K-means Clustering 11  Definition  K-means is a partition clustering algorithm based on iterative relocation that partitions a dataset into k clusters.  Objective  Locally minimizes sum of squared distance between the data points and their corresponding cluster centers:  Given a set of observations (x1, x2, …, xn); Cluster into k sets (k < n) X = {X1, X2, …, Xk}
  • 12. K-means Clustering (2) 12  Algorithm  Initialize k cluster centers randomly. Repeat until it converges:  Cluster Assignment Step: Assign each data point xi to the cluster fh such that distance of xi from center of fh is minimum  Center Re-estimation Step: Re-estimate each cluster center as the mean of the points in that cluster
  • 13. Content Introduction Image’s Features Clustering Algorithm Relevance Feedback Implementation and Evaluation Conclusions and Future Work 13
  • 14. Relevance feedback? 14  Motivation  The limitation of low-level image feature-based searching  Mechanism  After initial retrieval results are presented, allow the user to provide feedback on the relevance of one or more of the retrieved images.  Use this feedback information to reformulate the query.  Produce new results based on reformulated query.  Challenges  Require real-time processing  Training data set is small
  • 15. RF Architecture 15 Rankings CBIR System Ranked Images 1. Img1 2. Img2 3. Img3 . . 1. Img1  2. Img2  3. Img3  . . Feedback Query Image Revised Query Re-Ranked Images 1. Img2 2. Img4 3. Img5 . . Query Reformulation Images Database
  • 16. Support vector machine 16  Classification method  Given a set of training examples, each marked as belonging to one of two categories  An SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.  Linear Case  Training data  A separating hyperplane  Optimal separating hyperplane (OSH)
  • 17. Support vector machine (2) 17  Linear Case (cont.)  The classification function  Non-linear Case  The classification function  Kernels
  • 18. Content Introduction Image’s Features Clustering Algorithm Relevance Feedback Implementation and Evaluation Conclusions and Future Work 18
  • 19. Clustering Implementation 19  Clustering  Take feature vectors database as input  Apply K-means algorithm to cluster the database  Finding  Find appropriate cluster with the query image
  • 20. RF Implementation 20  Support vector machine classifier  Suitable when number of training data is small  Can be applied in a real-time system
  • 21. Environment & Parameters 21  Environment  9918 images with various kinds of images  Desktop computer: Intel Core 2 Dual 3.16 GHz, 4-GB RAM, Windows 7 Ultimate  Sun Java 1.6-u7  All components of the system are implemented by using Java  Parameters  Choose K=7 for K-means algorithm  Choose radical basis function (RBF) for support vector machine
  • 22. Clustering Evaluation 22  Accuracy  Clustering does not adversely affect the accuracy
  • 23. Clustering Evaluation 23  Searching time Applying clustering significantly improves the performance
  • 24. RF Evaluation 24  Accuracy  Improve the accuracy after several iterations
  • 25. Content Introduction Image’s Features Clustering Algorithm Relevance Feedback Implementation and Evaluation Conclusions and Future Work 25
  • 26. Conclusion 26  Achievements  Successfully build a complete content-based image retrieval system  The performance is significantly improved by applying K-means clustering algorithm to cluster image database  Using support vector machine in “Relevance Feedback” can remarkably increase the accuracy  Shortcomings  Low-level feature-based searching method depends on other authors’ method  Future works  Develop a low-level feature-based searching method that is suitable with each kind of images domain
  • 27. 27

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