Chu-Hong Hoi andChu-Hong Hoi and Michael R. Lyu Michael R. LyuDepartment of Computer Science and EngineeringDepartment of Computer Science and Engineering

The Chinese University of Hong Kong, Shatin, N.T.The Chinese University of Hong Kong, Shatin, N.T.,, Hong Kong SAR Hong Kong SAR{chhoi, lyu} @ cse.cuhk.edu.hk{chhoi, lyu} @ cse.cuhk.edu.hk

Department of Computer Science and Engineering, CUHKDepartment of Computer Science and Engineering, CUHK

17th International Conference on Pattern Recognition17th International Conference on Pattern Recognition Cambridge, UK, 23-26 August, 2004Cambridge, UK, 23-26 August, 2004

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 SVM ensembles technique. We show promising results from empirical evaluation with the regular method.

(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

Combination Strategy

• For each SVM ensemble, the sum rule is engaged.

• Each positive group is assigned with a weight.

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Binary-SVM Binary-SVM Binary-SVM Binary-SVM

Combiner of Group-1 Combiner of Group-2

Aggregating the Groups

Retrieval performance for “cars”

Retrieval performance for “roses”