Dual Transition Uniform Lbp Matrix for Efficient Image Retrieval

Kumar, V. Vijaya; Rao, A. Srinivasa; Krishna, YK Sundara

doi:10.5815/ijigsp.2015.08.06

Cited by 11 publications

(7 citation statements)

References 30 publications

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“…Recently the color texture analysis methods that integrate the color components with textural features have attracted significant attention [25,26,27]. The researchers' pachos [28] conducted the investigation on the role of color attributes in texture classification and they have experimented on 50 images using HSV, LAB and RGB color models and found that HSV color space is the best when compared to the other. The local binary pattern (LBP) [29] and Gabor filter descriptors are used with different color spaces using both parallel and integrative approaches on Outex-13, Outex-14 and MIT-VisTex color databases.…”

Section: Proposed Methodsmentioning

confidence: 99%

Classification of Textures Based on Ternary Transition Motif Matrix Features

Bhamidipati

Kumar

2019

IJRTE

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The various classification methods proposed in the literature are mostly extracted either local features or global features or color features. After an exhaustive study on various local, global and color descriptors this research found that one type of features may not yield good classification results. To address this paper, proposed new variant that integrates the color features derived from HSV color plane and transition based scan features. This paper initially converted RGB color plane image in to HSV color plane. The individual histograms of H, S and V plane are extracted as one of the features and these are named as pure color features. This paper derives a novel extension to the exiting motif frame works of the literature. The proposed ternary transition motif matrix (TTMM) is completely different from the existing motif frameworks and it is derived on the V-plane of the HSV color model. The TTTM scans the given 2x2 grid in a fixed format by visiting each pixel position exactly once. The TTMM derives a ternary transition pattern based on the relationship between grey level intensities of current pixel and its immediate pixel of scan instead of traversing on the incremental difference. The proposed TTMM derives a unique code for each 2x2 grid, ranging from 0 to 80 and replaces the 2x2 grid with this value. The co-occurrence matrix derived on this ternary transition motif (TTM) coded image is named as TTMM and it holds the spatial relationship between the TTM coded grids. The gray level co-occurrence matrix (GLCM) features derived on TTMM are integrated with the pure color features to derive final feature vector. This descriptor is applied on six popular databases using machine learning classifiers. The results are compared with existing motif based and other local based approaches. The results exhibit the high classification rate of the proposed method over the existing ones. .

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Section: Proposed Methodsmentioning

confidence: 99%

Classification of Textures Based on Ternary Transition Motif Matrix Features

Bhamidipati

Kumar

2019

IJRTE

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“…Uniform LBP is treated as the fundamental properties of texture image, and there is 80% to 85% of the texture images contain only Uniform LBP. 15) The number of uniform LBP patterns is P (P-1) + 2, P is the number of pixels in the field. For 8…”

Section: Uniform Lbpmentioning

confidence: 99%

Wear Debris Classification of Steel Production Equipment using Feature Fusion and Case-based Reasoning

et al. 2018

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Wear debris classification is of great significance for identifying machine wear states. In this paper, a method of wear debris classification using feature fusion and CBR is proposed. The method integrates local feature LBP, global feature FD and Tamura coarseness, and then the fused features are applied in CBR system with different weights and different similarity, which is adaptable, extendable, modular and fast. The results show that the subdivision of wear debris images into size 32*32 when calculating LBP is helpful for improving the classification, the combination of local features and global features can get better results. The comparative experimental results of different classification methods show that the CBR system has the shortest time-consuming while maintaining high classification accuracy.

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“…Texture plays an important role in image classification, retrieval, segmentation and age classification. In Literature various texture based methods are derived for image classification [ 15,16,17], face recognition [18,19,20], age and facial expressions identification [21,22], segmentation [23,24,25] image retrieval [26,27,28,29,30,31,32 ]. Texture is salient and indispensable feature for CBIR.…”

Section: Introductionmentioning

confidence: 99%

Image Retrieval using Quantized Local Binary Pattern

Latha¹,

Kumar²,

Obulesu³

2016

IJCA

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Image retrieval is one of the main topics in the field of computer vision and pattern recognition. Local descriptors are gaining more and more recognition in recent years as these descriptors are capable enough to identify the unique features, which suitably and uniquely describe any image for recognition and retrieval. One of the popular and efficient frame works for capturing texture information precisely is the Local binary pattern (LBP). LBP descriptors perform well in varying pose, illumination and lighting conditions. LBP is a structural approach and plays significant role in wide range of applications. One of the disadvantages with LBP based frame work is its dimensionality. The dimensionality of LBP increases, if one increases the number of neighboring pixels. Further statistical approaches gained lot of significance in image retrieval and LBP based methods raises high dimensionality and complexity issues, in deriving statistical features. The present paper addresses these two issues by quantizing the LBP code, to reduce dimensionality and by deriving GLCM features on quantized LBP. The proposed method is experimented on Corel database and compared with other existing methods. The experimental results indicate the high retrieval rate by the proposed method over the existing methods.

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Dual Transition Uniform Lbp Matrix for Efficient Image Retrieval

Cited by 11 publications

References 30 publications

Classification of Textures Based on Ternary Transition Motif Matrix Features

Classification of Textures Based on Ternary Transition Motif Matrix Features

Wear Debris Classification of Steel Production Equipment using Feature Fusion and Case-based Reasoning

Image Retrieval using Quantized Local Binary Pattern

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