title: SEMI-SUPERVISED OBJECT RECOGNITION USING STRUCTURE KERNEL.
abstract: Object recognition is a fundamental problem in computer vision. Part-based models offer a sparse, flexible representation of objects, but suffer from difficulties in training and often use standard kernels. In this paper, we propose a positive definite kernel called "structure kernel", which measures the similarity of two part-based represented objects. The structure kernel has three terms: 1) the global term that measures the global visual similarity of two objects; 2) the part term that measures the visual similarity of corresponding parts; 3) the spatial term that measures the spatial similarity of geometric configuration of parts. The contribution of this paper is to generalize the discriminant capability of local kernels to complex part-based object models. Experimental results show that the proposed kernel exhibit higher accuracy than state-of-art approaches using standard kernels.
authors.affiliation: Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
authors.forename: Botao
authors.initials: B
authors.lastname: Wang
authors.forename: Hongkai
authors.initials: H
authors.lastname: Xiong
authors.forename: Xiaoqian
authors.initials: X
authors.lastname: Jiang
authors.forename: Fan
authors.initials: F
authors.lastname: Ling
keywords: Object recognition; data mining; image features; kernel; machine learning