Chu-Hong Hoi and Michael R. Lyu Department of Computer Science and Engineering

1. 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