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Learn about a novel group-based relevance feedback algorithm utilizing SVM ensembles for content-based image retrieval tasks. The method considers multiple positive groups and one negative group, showing promising results. Experimental evaluation demonstrates improved retrieval performance for specific queries such as "cars" and "roses."
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17th International Conference on Pattern Recognition Cambridge, UK, 23-26 August, 2004 Group-based Relevance Feedback Support Vector Machine Ensembles With Chu-Hong Hoi and Michael R. Lyu Department of Computer Science and Engineering The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR {chhoi, lyu} @ cse.cuhk.edu.hk Architecture INTRODUCTION Support Vector Machines (SVM) have been proposed as an effective technique for relevance feedback tasks in Content-based Image Retrieval (CBIR). Regular SVM-based relevance feedback algorithms assume the problem as a strict binary-class classification problem. However, it is more reasonable and practical to regard the samples from multiple positive groups and one negative group. To formulate an effective algorithm, we propose a novel group-based relevance feedback (GRF) algorithm constructed with the SVMensembles technique. We showpromising results from empirical evaluation with theregular method. Proposed Scheme • Combination Strategy • For each SVM ensemble, the sum rule is engaged. • Each positive group is assigned with a weight. • (x+1)-class Assumption • Multiple positive groups and one negative group • Users are more interested in positive instances • Grouping irrelevant instances is tedious for users Interface of GRF Experimental Results Retrieval performance for “cars” Retrieval performance for “roses” Department of Computer Science and Engineering, CUHK
