PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Visual Category Recognition Using Spectral Regression and Kernel Discriminant Analysis
Muhammad Tahir, Josef Kittler, Fei Yan, Krystian Mikolajczyk, K.E.A. van de Sande and T Gevers
In: 2nd IEEE International Workshop on Subspace Methods, 27th Sept, Kyoto, Japan.

Abstract

Visual category recognition (VCR) is one of the most important tasks in image and video indexing. Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold learning. Recently, Spectral Regression combined with Kernel Discriminant Analysis (SR-KDA) has been successful in many classification problems. In this paper, we adopt this solution to VCR and demonstrate its advantages over existing methods both in terms of speed and accuracy. The distinctiveness of this method is assessed experimentally using an image and a video benchmark: the PASCAL VOC Challenge 08 and the Mediamill Challenge. From the experimental results, it can be derived that SR-KDA consistently yields significant performance gains when compared with the state-of-the art methods. The other strong point of using SR-KDA is that the time complexity scales linearly with respect to the number of concepts and the main computational complexity is independent of the number of categories.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:6673
Deposited By:Muhammad Tahir
Deposited On:08 March 2010