od - Content-Based Image Retrieval Reading Ones Mind and...

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Content-Based Image Retrieval: Reading One’s Mind and Making People Share Oral defense by Sia Ka Cheung Supervisor: Prof. Irwin King 31 July 2003
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31 July 2003 2 Flow of Presentation Content-Based Image Retrieval Reading One’s Mind Relevance Feedback Based on Parameter Estimation of Target Distribution Making People Share P2P Information Retrieval DIS tributed CO ntent-based V isual I nformation R etrieval
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31 July 2003 3 Content-Based Image Retrieval How to represent and retrieve images? By annotation (manual) Text retrieval Semantic level (good for picture with people, architectures) By the content (automatic) Color, texture, shape Vague description of picture (good for pictures of scenery and with pattern and texture)
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31 July 2003 4 Feature Extraction R B G
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31 July 2003 5 Indexing and Retrieval Images are represented as high dimensional data points (feature vector) Similar images are “close” in the feature vector space Euclidean distance is used
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31 July 2003 6 Typical Flow of CBIR Images Database Index and Storage Feature Extraction Query Result Query Image Lookup
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31 July 2003 7 Reading One’s Mind Relevance Feedback
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31 July 2003 8 Why Relevance Feedback? The gap between semantic meaning and low-level feature the retrieved results are not good enough Images Database Index and Storage Feature Extraction Result Query Image Lookup Better Result Feedback Better Result Feedback
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1 st  iteration User Feedback Display 2 nd  iteration Display User Feedback Estimation & Display selection Feedback to system
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31 July 2003 10 Problem Statement Assumption: images of the same semantic meaning/category form a cluster in feature vector space Given a set of positive examples, learn user’s preference and find better result in the next iteration
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31 July 2003 11 Former Approaches Multimedia Analysis and Retrieval System (MARS) IEEE Trans CSVT 1998 Weight updating, modification of distance function Pic-Hunter IEEE Trans IP 2000 Probability based, updated by Bayes’ rule Maximum Entropy Display
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31 July 2003 12 Comparisons Aspect Model Description Modeling of user’s target MARS Weighted Euclidean distance Pic-Hunter Probability associated with each image Our approach User’s target data point follow Gaussian distribution Learning method MARS Weight updating, modification of distance function Pic-Hunter Bayes’ rule Our approach Parameter estimation Display selection MARS K-NN neighborhood search Pic-Hunter Maximum entropy principle Our approach Simulated maximum entropy principle
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31 July 2003 13 Estimation of Target Distribution Assume the user’s target follows a Gaussian distribution Construct a distribution that best fits the relevant data points into some “specific” region Data points selected as relevant
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31 July 2003 14 Estimation of Target Distribution Assume the user’s target follows a Gaussian distribution Construct a distribution that best fits the relevant data points into some “specific” region Data points selected as relevant
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