Region filtering using color and texture features for image retrieval

Cheng Chieh Chiang, Ming Han Hsieh, Yi Ping Hung, Greg C. Lee

Research output: Contribution to journalConference article

7 Citations (Scopus)

Abstract

This paper presents a region-based image retrieval (RBIR) system in which users can choose specific regions as the query. Our goal is to assist the user to formulate more precise queries with which the retrieval system can focus on the user's interested part. In this work, images are partitioned into a set of regions by using the watershed segmentation. Color-size histogram and Gabor texture features are extracted from each watershed region. We propose a scheme of region filtering based on individual features, rather than integrating different features, to reduce the computational load of the image retrieval. This paper also defines the dissimilarity measure of images, and therefore relevance feedback is used for improving our retrieval. Finally we describe some experimental results of our RBIR system.

Original languageEnglish
Pages (from-to)487-496
Number of pages10
JournalLecture Notes in Computer Science
Volume3568
Publication statusPublished - 2005 Oct 17
Event4th International Conference on Image and Video Retrieval, CIVR 2005 - , Singapore
Duration: 2005 Jul 202005 Jul 22

Fingerprint

Texture Feature
Image retrieval
Image Retrieval
Filtering
Textures
Color
Watersheds
Retrieval
Query
Feedback
Dissimilarity Measure
Relevance Feedback
Histogram
Segmentation
Choose
Experimental Results

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Region filtering using color and texture features for image retrieval. / Chiang, Cheng Chieh; Hsieh, Ming Han; Hung, Yi Ping; Lee, Greg C.

In: Lecture Notes in Computer Science, Vol. 3568, 17.10.2005, p. 487-496.

Research output: Contribution to journalConference article

Chiang, Cheng Chieh ; Hsieh, Ming Han ; Hung, Yi Ping ; Lee, Greg C. / Region filtering using color and texture features for image retrieval. In: Lecture Notes in Computer Science. 2005 ; Vol. 3568. pp. 487-496.
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