Composite description based on color vector quantization and visual primary features for CBIR tasks
This paper presents a novel method for content-based color image retrieval that combines color vector quantization and visual primary features into a compact feature representation. Color vector quantization is proposed to describe the image in a compressed stream by preserving the contrast of an im...
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| Veröffentlicht in: | Multimedia tools and applications Jg. 80; H. 24; S. 33409 - 33427 |
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01.10.2021
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| Abstract | This paper presents a novel method for content-based color image retrieval that combines color vector quantization and visual primary features into a compact feature representation. Color vector quantization is proposed to describe the image in a compressed stream by preserving the contrast of an image and produce two color quantizers which are processed by vector quantization to preserve the content of a color image. Loosely inspired by human visual system and its mechanism in effectively recognizing objects in an image by its edges and color distribution, we propose extraction of visual primary features based on edge texture orientation and color moments features. The representation of the proposed method utilizes histogram-based features which are populated by color quantization and visual primary features that are used to measure the similarity between the two color images by a specific distance metric computation. The proposed method proves to be efficient and adaptive to the particulars of image retrieval, while it does not require any training information, making it suitable for real time color CBIR applications.The smaller feature size is an additional benefit of the proposed methodology which makes it ideal to use for embedded computing, mobile computing, energy efficient computing and high-throughput systems. An extensive experimental evaluation conducted on four publicly available datasets namely Wang, Vistex-640, Corel-5k and Corel-10k datasets against well-known fusion based, non-fusion based, and deep learning methods highlights the effectiveness and efficiency of the proposed method. |
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| AbstractList | This paper presents a novel method for content-based color image retrieval that combines color vector quantization and visual primary features into a compact feature representation. Color vector quantization is proposed to describe the image in a compressed stream by preserving the contrast of an image and produce two color quantizers which are processed by vector quantization to preserve the content of a color image. Loosely inspired by human visual system and its mechanism in effectively recognizing objects in an image by its edges and color distribution, we propose extraction of visual primary features based on edge texture orientation and color moments features. The representation of the proposed method utilizes histogram-based features which are populated by color quantization and visual primary features that are used to measure the similarity between the two color images by a specific distance metric computation. The proposed method proves to be efficient and adaptive to the particulars of image retrieval, while it does not require any training information, making it suitable for real time color CBIR applications.The smaller feature size is an additional benefit of the proposed methodology which makes it ideal to use for embedded computing, mobile computing, energy efficient computing and high-throughput systems. An extensive experimental evaluation conducted on four publicly available datasets namely Wang, Vistex-640, Corel-5k and Corel-10k datasets against well-known fusion based, non-fusion based, and deep learning methods highlights the effectiveness and efficiency of the proposed method. |
| Author | Asif, M. Daud Abdullah Zhou, Jun Wang, Jing Gao, Yongsheng |
| Author_xml | – sequence: 1 givenname: M. Daud Abdullah surname: Asif fullname: Asif, M. Daud Abdullah email: daud.asif@griffithuni.edu.au organization: School of Engineering, Griffith University – sequence: 2 givenname: Jing surname: Wang fullname: Wang, Jing organization: School of Information and Communication Technology, Griffith University – sequence: 3 givenname: Yongsheng surname: Gao fullname: Gao, Yongsheng organization: School of Engineering, Griffith University – sequence: 4 givenname: Jun surname: Zhou fullname: Zhou, Jun organization: School of Information and Communication Technology, Griffith University |
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| CitedBy_id | crossref_primary_10_1007_s42979_024_03009_7 crossref_primary_10_1155_2023_6257573 crossref_primary_10_1016_j_eswa_2023_120666 crossref_primary_10_3390_electronics11020202 |
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| SubjectTerms | Color imagery Computer Communication Networks Computer Science Counters Data Structures and Information Theory Datasets Feature extraction Histograms Image contrast Image management Image retrieval Mobile computing Multimedia Information Systems Object recognition Representations Special Purpose and Application-Based Systems Systems analysis Vector quantization |
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| Title | Composite description based on color vector quantization and visual primary features for CBIR tasks |
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