K-means based histogram using multiresolution feature vectors for color texture database retrieval

Cong Bai 1 Jinglin Zhang 2 Zhi Liu 3, 4 Wan-Lei Zhao 5
4 Sirocco - Analysis representation, compression and communication of visual data
IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE, Inria Rennes – Bretagne Atlantique
5 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : Color and texture are two important features in content-based image retrieval. It has been shown that using the combination of both could provide better performance. In this paper, a K-means based histogram (KBH) using the combination of color and texture features for image retrieval is proposed. Multiresolution feature vectors representing color and texture features are directly generated from the coefficients of Discrete Wavelet Transform (DWT), and K-means is exploited to partition the vector space with the objective to reduce the number of histogram bins. Thereafter, a fusion of z-score normalized Chi- Square distance between KBHs is employed as the similarity measure. Experiments have been conducted on four natural color texture data sets to examine the sensitivity of KBH to its parameters. The performance of the proposed approach has been compared with state-of-the-art approaches. Results evaluated in terms of Precision-Recall and Average Retrieval Rate (ARR) show that our approach outperforms the referred approaches.
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https://hal.inria.fr/hal-00997873
Contributor : Zhi Liu <>
Submitted on : Thursday, May 29, 2014 - 3:44:17 PM
Last modification on : Thursday, November 15, 2018 - 11:58:14 AM

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Cong Bai, Jinglin Zhang, Zhi Liu, Wan-Lei Zhao. K-means based histogram using multiresolution feature vectors for color texture database retrieval. Multimedia Tools and Applications, Springer Verlag, 2014, ⟨10.1007/s11042-014-2053-8⟩. ⟨hal-00997873⟩

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