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Indexed by:期刊论文

Date of Publication:2013-11-23

Journal:NEUROCOMPUTING

Included Journals:EI、SCIE、Scopus

Volume:120

Issue:,SI

Page Number:203-213

ISSN No.:0925-2312

Key Words:Object recognition; Superpixels; Local learning; Neighbor integration

Abstract:In this paper, we propose a simple yet effective method for superpixel level object recognition on the bag-of-feature framework. Instead of using general classifiers for the superpixel categorization, we introduce local learning classifiers into our framework, which aims to turn a highly non-linear classification problem into multiple local linear problems within different subsets of the database, so as to tackle the intraclass variation problem brought by superpixel based representations of objects. In addition, context information is used to make better performance by combining each superpixel with its appearance-based superpixel neighbors within a certain neighborhood distance from superpixel mean color map. At last, we utilize superpixel based Graph Cuts algorithm to segment the objects from background image. We test the proposed method on Graz-02 dataset, and get results comparable to the state-of-the-art. (c) 2013 Elsevier B.V. All rights reserved.