A novel hierarchical approach to image retrieval using color and spatial information

Xiuqi Li, Shu Ching Chen, Mei Ling Shyu, Sheng-Tun Li, Borko Furht

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

A novel hierarchical approach to image retrieval is proposed. First, a color label histogram is used to effectively filter out the images that are not similar to the query image in color. The proposed color label histogram built by categorizing the pixel colors is computationally much more efficient compared to other approaches. Next, the class parameters of those images passing the first filter are used to identify the images similar to the query image in spatial layout. These class parameters are obtained automatically from the proposed unsupervised segmentation algorithm. Moreover, the wavelet decomposition coefficients are used to generate the initial partition for the segmentation algorithm. It doubles the segmentation performance. At the last stage, all images passing two filters are ranked based on the total normalized distance in color and spatial layout. The experiments show the effectiveness and efficiency of our approach.

Original languageEnglish
Title of host publicationAdvances in Multimedia Information Processing - PCM 2002 - 3rd IEEE Pacific Rim Conference on Multimedia, Proceedings
EditorsYung-Chang Chen, Long-Wen Chang, Chiou-Ting Hsu
PublisherSpringer Verlag
Pages175-182
Number of pages8
ISBN (Print)3540002626, 9783540002628
Publication statusPublished - 2002 Jan 1
Event3rd IEEE Pacific Rim Conference on Multimedia, PCM 2002 - Hsinchu, Taiwan
Duration: 2002 Dec 162002 Dec 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2532
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd IEEE Pacific Rim Conference on Multimedia, PCM 2002
CountryTaiwan
CityHsinchu
Period02-12-1602-12-18

Fingerprint

Spatial Information
Image retrieval
Image Retrieval
Color
Segmentation
Filter
Labels
Histogram
Layout
Wavelet decomposition
Query
Wavelet Decomposition
Pixels
Pixel
Partition
Coefficient
Experiments
Experiment

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Li, X., Chen, S. C., Shyu, M. L., Li, S-T., & Furht, B. (2002). A novel hierarchical approach to image retrieval using color and spatial information. In Y-C. Chen, L-W. Chang, & C-T. Hsu (Eds.), Advances in Multimedia Information Processing - PCM 2002 - 3rd IEEE Pacific Rim Conference on Multimedia, Proceedings (pp. 175-182). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2532). Springer Verlag.
Li, Xiuqi ; Chen, Shu Ching ; Shyu, Mei Ling ; Li, Sheng-Tun ; Furht, Borko. / A novel hierarchical approach to image retrieval using color and spatial information. Advances in Multimedia Information Processing - PCM 2002 - 3rd IEEE Pacific Rim Conference on Multimedia, Proceedings. editor / Yung-Chang Chen ; Long-Wen Chang ; Chiou-Ting Hsu. Springer Verlag, 2002. pp. 175-182 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "A novel hierarchical approach to image retrieval is proposed. First, a color label histogram is used to effectively filter out the images that are not similar to the query image in color. The proposed color label histogram built by categorizing the pixel colors is computationally much more efficient compared to other approaches. Next, the class parameters of those images passing the first filter are used to identify the images similar to the query image in spatial layout. These class parameters are obtained automatically from the proposed unsupervised segmentation algorithm. Moreover, the wavelet decomposition coefficients are used to generate the initial partition for the segmentation algorithm. It doubles the segmentation performance. At the last stage, all images passing two filters are ranked based on the total normalized distance in color and spatial layout. The experiments show the effectiveness and efficiency of our approach.",
author = "Xiuqi Li and Chen, {Shu Ching} and Shyu, {Mei Ling} and Sheng-Tun Li and Borko Furht",
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Li, X, Chen, SC, Shyu, ML, Li, S-T & Furht, B 2002, A novel hierarchical approach to image retrieval using color and spatial information. in Y-C Chen, L-W Chang & C-T Hsu (eds), Advances in Multimedia Information Processing - PCM 2002 - 3rd IEEE Pacific Rim Conference on Multimedia, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2532, Springer Verlag, pp. 175-182, 3rd IEEE Pacific Rim Conference on Multimedia, PCM 2002, Hsinchu, Taiwan, 02-12-16.

A novel hierarchical approach to image retrieval using color and spatial information. / Li, Xiuqi; Chen, Shu Ching; Shyu, Mei Ling; Li, Sheng-Tun; Furht, Borko.

Advances in Multimedia Information Processing - PCM 2002 - 3rd IEEE Pacific Rim Conference on Multimedia, Proceedings. ed. / Yung-Chang Chen; Long-Wen Chang; Chiou-Ting Hsu. Springer Verlag, 2002. p. 175-182 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2532).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - A novel hierarchical approach to image retrieval using color and spatial information

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AU - Chen, Shu Ching

AU - Shyu, Mei Ling

AU - Li, Sheng-Tun

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N2 - A novel hierarchical approach to image retrieval is proposed. First, a color label histogram is used to effectively filter out the images that are not similar to the query image in color. The proposed color label histogram built by categorizing the pixel colors is computationally much more efficient compared to other approaches. Next, the class parameters of those images passing the first filter are used to identify the images similar to the query image in spatial layout. These class parameters are obtained automatically from the proposed unsupervised segmentation algorithm. Moreover, the wavelet decomposition coefficients are used to generate the initial partition for the segmentation algorithm. It doubles the segmentation performance. At the last stage, all images passing two filters are ranked based on the total normalized distance in color and spatial layout. The experiments show the effectiveness and efficiency of our approach.

AB - A novel hierarchical approach to image retrieval is proposed. First, a color label histogram is used to effectively filter out the images that are not similar to the query image in color. The proposed color label histogram built by categorizing the pixel colors is computationally much more efficient compared to other approaches. Next, the class parameters of those images passing the first filter are used to identify the images similar to the query image in spatial layout. These class parameters are obtained automatically from the proposed unsupervised segmentation algorithm. Moreover, the wavelet decomposition coefficients are used to generate the initial partition for the segmentation algorithm. It doubles the segmentation performance. At the last stage, all images passing two filters are ranked based on the total normalized distance in color and spatial layout. The experiments show the effectiveness and efficiency of our approach.

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M3 - Conference contribution

SN - 3540002626

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Li X, Chen SC, Shyu ML, Li S-T, Furht B. A novel hierarchical approach to image retrieval using color and spatial information. In Chen Y-C, Chang L-W, Hsu C-T, editors, Advances in Multimedia Information Processing - PCM 2002 - 3rd IEEE Pacific Rim Conference on Multimedia, Proceedings. Springer Verlag. 2002. p. 175-182. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).