QUT ePrints

Image Retrieval Using Circular Hidden Markov Models with a Garbage State

Cai, Jinhai, Ee, Dominic, & Smith, Robert (2007) Image Retrieval Using Circular Hidden Markov Models with a Garbage State. In Image and Vision Computing New Zealand 2007, 5-7 December, Hamilton, New Zealand.

Abstract

Shape-based image and video retrieval is an active research topic in multimedia information retrieval. It is well known that there are significant variations in shapes of the same category extracted from images and videos. In this paper, we propose to use circular hidden Markov models for shape recognition and image retrieval. In our approach, we use a garbage state to explicitly deal with shape mismatch caused by shape deformation and occlusion. We will propose a modi¯ed circular hidden Markov model (HMM)for shape-based image retrieval and then use circular HMMs with a garbage state to further improve the performance.

To evaluate the proposed algorithms, we have conducted experiments using the database of the MPEG-7 Core Experiments Shape-1, Part B. The experiments show that our approaches are robust to shape deformations such as shape variations and occlusion. The performance of our approaches is comparable to that of the state-of-the-art shape-based image retrieval systems in terms of accuracy and speed.

Impact and interest:

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:

250 since deposited on 19 Dec 2007
8 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: 11229
Item Type: Conference Paper
Additional Information: For more information, please refer to the conference website (see hypertext link) or contact the authors.
Additional URLs:
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)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2007 (please consult authors)
Deposited On: 19 Dec 2007
Last Modified: 29 Feb 2012 23:38

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page