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Unformatted text preview: Hierarchical Image Feature Extraction And Classification Min-Hsuan Tsai Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, 1308 W Main Street, Urbana, IL 61801-2307 [email protected] Shen-Fu Tsai Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, 1308 W Main Street, Urbana, IL 61801-2307 [email protected] Thomas S. Huang Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, 1308 W Main Street, Urbana, IL 61801-2307 [email protected] ABSTRACT In the field of machine learning and pattern recognition, an alternative to conventional classification is hierarchical clas- sification that exploits hierarchical relations between con- cepts of interest. To the best of our knowledge, all hierar- chical classification methods in the literature are designed to reduce computation complexity without sacrificing too much on accuracy performance. In this work on image clas- sification, we first propose a hierarchical image feature ex- traction that extracts image feature based on the location of current node in hierarchy to fit the images under current node and to better distinguish its subclasses. As far as we know, such node-dependent feature extraction has not been considered in the literature. Contrary to former hierarchi- cal classification methods that only consider local structure of the hierarchy, we propose a novel cross-level hierarchical classification method that utilizes both global and local con- cept structures throughout the entire path decision-making process. Our experimental result on two datasets shows that the proposed hierarchical feature extraction combined with our novel hierarchical classification achieves better accuracy performance than conventional non-hierarchical classifica- tion methods, and hence conventional hierarchical methods as well. Categories and Subject Descriptors I.4.8 [ Image processing and computer vision ]: Scene Analysis; I.2.10 [ Artificial Intelligence ]: Vision and Scene Understanding General Terms Algorithms, Experimentation Keywords Hierarchical classification, Hierarchical feature extraction, Image classification Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. MM’10, October 25–29, 2010, Firenze, Italy. Copyright 2010 ACM 978-1-60558-933-6/10/10 ...$10.00. 1. INTRODUCTION In the field of computer vision and multimedia processing, the semantic gap between high-level concepts and low-level features is a major obstacle to classification related tasks....
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- Spring '08
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