Choice of a pertinent color space for color texture characterization using parametric spectral analysis

Qazi, Imtnan-Ul-Haque; Alata, Olivier; Burie, Jean-Christophe; Moussa, Ahmed; Fernández-Maloigne, Christine

doi:10.1016/j.patcog.2010.07.007

Cited by 50 publications

(31 citation statements)

References 21 publications

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“…In the sequence, to provide a better evaluation of the proposed signatures, we performed a comparison with other color texture methods found in the literature. For this comparison, we considered the following methods: Gabor EEE [53,54], Histogram ratio features (HRF) [55], MultiLayer CCR [56], Linear prediction model [57,46] and an LBP þHaralick method [48]. A brief description of each method is presented as follows.…”

Section: Discussionmentioning

confidence: 99%

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Color texture analysis based on fractal descriptors

Backes

Casanova

Bruno

2012

Pattern Recognition

156

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Section: Discussionmentioning

confidence: 99%

“…It is a more recent approach and have many good papers (e.g. [46][47][48]). On the other hand, we have the older, but not less suitable, marginal approaches.…”

Section: Image Color Information-a Multi-spectral Approachmentioning

confidence: 99%

Color texture analysis based on fractal descriptors

Backes

Casanova

Bruno

2012

Pattern Recognition

156

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“…Furthermore, in this paper, we only considered the RGB color system. In the future, we would like to extend the existing work by doing a comprehensive and detailed comparative study of these descriptors in other color systems, such as those proposed in [35] and [36].…”

Section: Discussionmentioning

confidence: 99%

Matching of Tracked Pedestrians Across Disjoint Camera Views Using CI-DLBP

Lian

Lai

Suen

et al. 2012

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-Matching pedestrians across disjoint camera views is a challenging task, since their observations are separated in time and space and their appearances may vary considerably. Recently, some approaches of matching pedestrians have been proposed. However, these approaches either used too complex representations or only considered the color information and discarded the spatial structural information of the pedestrian. In order to describe the spatial structural information in color space, we propose a distance-based local binary pattern (DLBP) descriptor. Besides the spatial structural information, the color itself namely its intensity value is also an important feature in matching pedestrians across disjoint camera views. In order to effectively combine these two kinds of information, we further propose a novel CI − DLBP descriptor, which unifies the color intensity and DLBP by learning the joint distributions (2-D histograms) of the DLBP and color intensity at each channel. In addition, different from the previous approaches in which the pedestrians matching is based on their whole bodies, we develop a part-based pedestrian representation because the color density and spatial structural information between the upper outer garment and the lower garment worn by the pedestrian is usually different. Experimental results on challenging realistic scenarios and VIPeR dataset validate the proposed DLBP operator, the CI − DLBP descriptor, and the part-based pedestrian representation for pedestrian matching across disjoint camera views. Compared with existing methods based on color information, this new CI − DLBP approach performs better.Index Terms-CI − DLBP descriptor, disjoint camera views, distance-based local binary pattern (DLBP) operator, part-based pedestrian representation, pedestrian matching.

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“…The color images contain richer image information, and have many kinds of color space presentations, so the key of the color image segmentation lies in how to utilize the rich color information to achieve the purpose of segmentation [10]. The color of timber is determined by the lignin in it, so similarly the amount of lignin in bamboo wood will also determine the generation and changes of colors in its surface.…”

Section: Feature Extraction Based On Lab Color Spacementioning

confidence: 99%