Publication Type

Journal Article

Version

publishedVersion

Publication Date

7-2014

Abstract

Hierarchical classification (HC) is a popular and efficient way for detecting the semantic concepts from the images. The conventional method always selects the branch with the highest classification response. This branch selection strategy has a risk of propagating classification errors from higher levels of the hierarchy to the lower levels. We argue that the local strategy is too arbitrary, because the candidate nodes are considered individually, which ignores the semantic and context relationships among concepts. In this paper, we first propose a novel method for HC, which is able to utilize the semantic relationship among candidate nodes and their children to recover the responses of unreliable classifiers of the candidate nodes. Thus the error is expected to be reduced by a collaborative branch selection scheme. The approach is further extended to enable multiple branch selection, where other relationships (e.g., contextual information) are incorporated, with the hope of providing the branch selection a more globally valid, semantically and contextually consistent view. An extensive set of experiments on three large-scale datasets shows that the proposed methods outperform the conventional HC method, and achieve a satisfactory balance between the effectiveness and efficiency. (C) 2014 Elsevier Inc. All rights reserved.

Keywords

Concept detection, Large-scale hierarchy, Error propagation

Discipline

Computer Sciences | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Publication

Computer Vision and Image Understanding

Volume

124

First Page

79

Last Page

90

ISSN

1077-3142

Identifier

10.1016/j.cviu.2014.03.010

Publisher

Elsevier

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