Texture Classification Using Complete Texton Matrix

Kumari, Y. Sowjanya; Kumar, V. Vijaya; Satyanarayana, Ch.

doi:10.5815/ijigsp.2017.10.07

Cited by 8 publications

(5 citation statements)

References 30 publications

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“…For the sake of simplicity, we only list the average classification rates. It shows that the average classification accuracy of our method is as high as 98.42%, which far exceeds the results of literature [6]- [8], slightly better than that of literature [9].…”

Section: Experimental Results and Analysiscontrasting

confidence: 62%

A New Illumination-Rotation-Invariance Texture Feature Based on Quasi-Periodic Signal Analysis

2020

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Texture classification is a classic problem in pattern recognition. It is an effective strategy for improving texture classification to find the texture features with both powerful discrimination and various invariant properties. In this paper, we provide a new insight into texture images, that is, texture images can be treated as quasi-periodic signals. Some new concepts such as Dominant Period Component (DPC), periodic degree (PD), and Main Frequency (MF) are proposed to characterize the properties of quasi-periodic signals. DPC controls the oscillation rate of a quasi-periodic signal and plays a key role in controlling the behavior of the whole signal. So it can serve as a key feature for texture classification. Based on this idea, we propose a new method to extract texture features. The proposed features have both powerful classification ability and rotation-illumination-invariance as well as robustness to noise. Experimental results on three texture data sets demonstrate the validity of this method. INDEX TERMS Quasi-periodic signal, main-frequency, texture feature, Hilbert marginal spectrum.

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Section: Experimental Results and Analysiscontrasting

confidence: 62%

A New Illumination-Rotation-Invariance Texture Feature Based on Quasi-Periodic Signal Analysis

2020

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“…The MTH derived multi texton histogram over the image and the histogram features are used for CBIR. The MTH creates ambiguity in identifying texton types due to its derivation of limited number of texton types with two identical pixels over a 2X2 grid [9,19].…”

Section: Related Workmentioning

confidence: 99%

“…The texton based methods [9][10][11][12] attained good results in texture classification and also on CBIR. A texton is derived if and only if two or more pixels in the grid exhibit the similar intensities.…”

Section: Introductionmentioning

confidence: 99%

A New Frame Work for Content Based Image Retrieval Based on Rule Based Motifs on Full Texton Images

G¹

2019

IJATCSE

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This paper derives new framework for content based image retrieval (CBIR) by deriving color features and texture features. This paper initially transforms the RGB color plane image into HSV color plane and derives the individual histograms on H, S and V planes. This paper derives rule based dynamic motif matrix (RDMM) on full texton index (FTi) index images of V-plane. The FTi overcomes the disadvantages and ambiguity issues in indexing textons of earlier methods. The RDMM overcomes the ambiguity issues arise especially when two or more pixels of the 2x2 grid exhibit the similar intensity levels and this reduces overall retrieval rate. This paper integrated the co-occurrence features derived on RDMM-FTi with histogram features of pure color model. The proposed framework is tested on popular color databases and also compared with state of art models of texton and motif based methods based on average precision rate (APR) and average recall rate(ARR). The experimental results indicate the efficacy of the proposed method over the existing methods.

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“…The purpose of textons is to use basic primitives to describe complex structures. Textons have been applied in a variety of ways, such as seen in Multi-Texton Histogram (MTH) [18], Texton Co-occurrence Matrix (TCM) [19], Complete Texton Matrix (CTM) [20] and Complete Multi Texton Histogram [21] with generally good results. However, the rigid nature of the textons makes it undesirable at some point [22].…”

Section: Introductionmentioning

confidence: 99%

Effective Image Representation using Double Colour Histograms for Content-Based Image Retrieval

Martey¹,

Lei

et al. 2021

IJCAI

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Image representation is critical to the successful realisation of Content-Based Image Retrieval (CBIR) systems. The choice of features to represent the image affects retrieval performance. Nowadays, image databases are heterogeneous and different feature types can be used for appropriate descriptions. This paper proposes an image representation for CBIR that combines Stacked Colour Histogram (SCH) and Conventional Colour Histogram (CCH) to improve image retrieval precision. This presented technique is designed to capture the colour and texture information of the image. The colour properties of an image are represented by CCH and that of texture by SCH. The weighted similarity measure is used to estimate the proportion of similarity values in the retrieval task. The novel descriptor has been widely tested on four standard image datasets, namely Batik, Coil100, Corel10K and Outext. Batik, Coil100 and Outext are used to assess texture discrimination. Corel10K is used to assess the discrimination of heterogeneous images. Experimental results and comparisons with SCH, CMTH, MTH, TCM, CTM and NRFUCTM demonstrate that the proposed descriptor has superior retrieval performance. Povzetek: V članku je podana učinkovita predstavitev slik z barvnim diagramom z namenom iskanja vsebine na slikah.

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Texture Classification Using Complete Texton Matrix

Cited by 8 publications

References 30 publications

A New Illumination-Rotation-Invariance Texture Feature Based on Quasi-Periodic Signal Analysis

A New Illumination-Rotation-Invariance Texture Feature Based on Quasi-Periodic Signal Analysis

A New Frame Work for Content Based Image Retrieval Based on Rule Based Motifs on Full Texton Images

Effective Image Representation using Double Colour Histograms for Content-Based Image Retrieval

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