QUT ePrints

Image Mining and Retrieval Using Hierarchical Support Vector Machines

Brown, Ross A. & Pham, Binh L. (2005) Image Mining and Retrieval Using Hierarchical Support Vector Machines. In Chen, Yi-Ping (Ed.) 11th International Conference on Multi-Media Modeling, Jan, Melbourne, Australia.

View at publisher

Abstract

For some time now, image retrieval approaches have been developed that use low-level features, such as colour histograms, edge distributions and texture measures. What has been lacking in image retrieval approaches is the development of general methods for more structured object recognition. This paper describes in detail a general hierarchical image classifier approach, and illustrates the ease with which it can be trained to find objects in a scene. To further illustrate the wide capabilities of this approach, results from its application to particle picking in biology and Vietnamese art image retrieval are listed.

Impact and interest:

3 citations in Scopus
Search Google Scholar™
0 citations in Web of Science®

Citation countsare sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

456 since deposited on 21 Oct 2005
110 in the past twelve months

Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 2273
Item Type: Conference Paper
Keywords: image mining, image retrieval, support vector machines
DOI: 10.1109/MMMC.2005.48
ISSN: 1550-5502
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Information Retrieval and Web Search (080704)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2005 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 21 Oct 2005
Last Modified: 29 Feb 2012 23:12

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page