Rotation-invariant colour texture classification through multilayer CCR

The aim of this paper is to describe a general framework for texture analysis which we refer to as the HEP (histograms of equivalent patterns). The HEP, of which we give a clear and unambiguous mathematical definition, is based on partitioning the feature space associated to image patches of predefined shape and size. This task is approached by defining, a priori, suitable local or global functions of the pixels' intensities. In a comprehensive survey we show that diverse texture descriptors, such as co-occurrence matrices, gray-level differences and local binary patterns, can be seen all to be examples of the HEP. In the experimental part we comparatively evaluate a comprehensive set of these descriptors on an extensive texture classification task. Within the class of HEP schemes, improved local ternary patterns (ILTP) and completed local binary patterns (CLBP) emerge as the best of parametric and nonparametric methods, respectively. The results also show the following patterns: (1) higher effectiveness of multi-level discretization in comparison with binarization; (2) higher F. Bianconi performed this work as a visiting researcher in the accuracy of parametric methods when compared to nonparametric ones; (3) a general trend of increasing performance with increasing dimensionality; and (4) better performance of point-to-average thresholding against point-topoint thresholding.

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“…It was later on extended to grey-scale images through a preliminary thresholding step [99], and, recently, to colour images too [5].…”

Section: Coordinated Clusters Representationmentioning

confidence: 99%

Texture Description Through Histograms of Equivalent Patterns

2012

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“…The multilayer coordinated cluster representation (multilayer CCR) 62 is an extension of the CCR (Refs. 63 and 64) to color images.…”

Section: Multilayer Coordinate Clusters Representationsmentioning

confidence: 99%

Theoretical and experimental comparison of different approaches for color texture classification

Bianconi

Harvey

Southam

et al. 2011

J. Electron. Imaging

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Color texture classification has been an area of intensive research activity. From the very onset, approaches to combining color and texture have been the subject of much discussion, and in particular, whether they should be considered joint or separately. We present a comprehensive comparison of the most prominent approaches both from a theoretical and experimental standpoint. The main contributions of our work are: (i) the establishment of a generic and extensible framework to classify methods for color texture classification on a mathematical basis, and (ii) a theoretical and experimental comparison of the most salient existing methods. Starting from an extensive set of experiments based on the Outex dataset, we highlight those texture descriptors that provide good accuracy along with low dimensionality. The results suggest that separate color and texture processing is the best practice when one seeks for optimal compromise between accuracy and limited number of features. We believe that our work may serve as a guide for those who need to choose the appropriate method for a specific application, as well as a basis for the development of new methods.

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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%