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Intelligent Image Database Searching system Jelena Te ic Advisor B S Manjunath ECE 277A project presentation Project Goal n Bridge between n Categorical Image Search Engine Sumengen Newsam Vision Research Lab UCSB n n n n Intelligent Image Database Search Engine Vision Group University of Bristol UK Improve an image query system based on user s response Use of user s feedback for Neural Network Training Queries are iteratively refined Semantics Based WWW Image Search Engine n n n n n Semantic classification of images Categorical indexing of images and associated text Combining text and image primitives such as color and texture for efficient search and retrieval Using relevance feedback to help users find what they are searching for http maya ece ucsb edu cgi bin imsearch main cgi Research Directions n Database indexing n n n Better retrieval results n n n User s feedback Incorporate a perceptual dissimilarity measure information into access building process Preprocessing step increases overall performance n n combination with existing indexing methods clustering multiple features Data statistics Keep it fairly simple and scalable Intelligent Image Database Searching n n n n Computer vision system for large WWW databases User defines objects of interest and similar objects in the database are then found automatically and returned as a set of thumbnails The user then selects which of these match his search This allows refinement of the search as further positive and negative examples of the object of interest Feature based search n Hard to determine a set of useful features and their relationship local global features n n n Relevant feature selection is automatic Exhaustively seek for useful subsets is computationally expensive Single class classification system that its progressively tuned in its ability to identify the class of relevant objects Implementation Classification using NN n Multi Layer Perceptron networks n n n Requires large set of classified training



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