PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Approximated classification in interactive facial image retrieval
Zhirong Yang and Jorma Laaksonen
In: 14th Scandinavian Conference on Image Analysis, 19-22 Jun 2005, Joensuu, Finland.

Abstract

For databases of facial images, where each subject is depicted in only one or a few images, the query precision of interactive retrieval suffers from the problem of extremely small class sizes. A potential way to address this problem is to employ automatic even though imperfect classification on the images according to some high level concepts. In this paper we point out that significant improvement in terms of the occurrence of the first subject hit is feasible only when the classifiers are of sufficient accuracy. In this work Support Vector Machines (SVM) are incorporated in order to obtain high accuracy for classifying the imbalanced data. We also propose an automatic method to choose the penalty factor of training error and the width parameter of the radial basis function used in training the SVM classifiers. More significant improvement in the speed of retrieval is feasible with small classes than with larger ones. The results of our experiments suggest that the first subject hit can be obtained two to five times faster for semantic classes such as "black persons" or "eyeglass-wearing persons".

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
User Modelling for Computer Human Interaction
Information Retrieval & Textual Information Access
ID Code:1728
Deposited By:Jorma Laaksonen
Deposited On:28 November 2005