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Image representation using complete multi-texton histogram

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Abstract

Texture is a fundamental aspect which is often used to represent the visual content of images. Psychologists stated that color and texture have a close relationship via fundamental micro-structures called textons. Textons are considered as atoms for pre-attentive human visual perception. Based on texton theory, many literature works have tried to develop efficient models for image recognition. In this paper, we put forward a texton-based method, called Complete Multi-Texton Histogram (CMTH), that has the ability to discriminate both texture and non-texture color images. CMTH incorporates information about the color, edge orientation and texton distribution within the image. The proposed CMTH has been extensively examined on five publicly available datasets. Three of these datasets were intended to evaluate texture discrimination, namely: Vistex, Outex, and Batik whereas the two others were intended to evaluate heterogeneous image discrimination, namely: Corel10K and UKBench. The proposed method has been evaluated via image classification and retrieval tasks. The obtained results have shown that our proposed descriptor significantly outperforms the state of the art methods in both classification and retrieval.

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Authors and Affiliations

  1. Dept. Computer Science and Information Technologies, University of Kasdi Merbah Ouargla (UKMO), Ghardaia Road, BP.511, 30 000, Ouargla, Algeria

    Belal Khaldi & Oussama Aiadi

  2. Laboratoire de recherche en Mathématiques et Informatique Appliquées (LAMIA), 3351, Boulevard des Forges, C.P. 500, Trois-Rivières, G9A 5H7, Canada

    Kherfi Mohammed Lamine

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  1. Belal Khaldi
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  2. Oussama Aiadi
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  3. Kherfi Mohammed Lamine
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Correspondence to Belal Khaldi.

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Khaldi, B., Aiadi, O. & Lamine, K.M. Image representation using complete multi-texton histogram. Multimed Tools Appl 79, 8267–8285 (2020). https://doi.org/10.1007/s11042-019-08350-1

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