Face Recognition based on Cross Diagonal Complete Motif Matrix

Reddy, A. Mallikarjuna; Krishna, V. Venkata; Sumalatha, L.

doi:10.5815/ijigsp.2018.03.07

Cited by 13 publications

(6 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…TTMM GF is rotational invariant and robust and holds the rich texture information. LTP [41] CLBP-SMC [42] CS-LBP [43] MCM [14] MMCM [21] [LMP-CM [22] Proposed TTMMH Proposed TTMMGF…”

Section: Discussionmentioning

confidence: 99%

Classification of Textures Based on Ternary Transition Motif Matrix Features

Bhamidipati

Kumar

2019

IJRTE

View full text Add to dashboard Cite

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. .

show abstract

“…TTMM GF is rotational invariant and robust and holds the rich texture information. LTP [41] CLBP-SMC [42] CS-LBP [43] MCM [14] MMCM [21] [LMP-CM [22] Proposed TTMMH Proposed TTMMGF…”

Section: Discussionmentioning

confidence: 99%

Classification of Textures Based on Ternary Transition Motif Matrix Features

Bhamidipati

Kumar

2019

IJRTE

View full text Add to dashboard Cite

show abstract

“…The texture features derived based on the local pattern or shape information are also popular in the CBIR. Among these the texton (32)(33)(34)(35)(36) and motif based (37)(38)(39) methods attained good results. They basically derived local pattern information on a 2 x 2 grid.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…The OE-TTM integrated the structural, texture and statistical features of an image and has shown noise resistance and improved discrimination abilities. Numerous extensions are proposed in the literature for another prominent structural pattern known as motif (37)(38)(39) that are shown improvement over preceding descriptors. The texture features are also derived based on the symmetric relationship between neighboring pixels of a 3 x 3 grid.…”

Section: Introductionmentioning

confidence: 99%

Color and cross diagonal symmetric pair co-occurrence matrix

Srinivas¹

2020

IJST

View full text Add to dashboard Cite

Objectives: The main goal of this study is to derive an efficient feature vector to capture both the color and texture information. This makes the proposed descriptor as a multipurpose descriptor. Methodology: This study derived color information by combining the individual histograms of H, S and V plane. A texture descriptor is derived by considering the relationship: i) between pairs of symmetric cross pixels and ii) between pairs of symmetric diagonal pixels of the 3 x 3 neighborhood. The relationship is derived using XOR and AND logical functions. Findings: This study derived a unique six bit code and constructed a co-occurrence matrix. The GLCM features are derived on this. To reduce the complexity, this study also derived another descriptor by indexing the six bit code using the rotational invariant property. Novelty: The proposed color and texture descriptors give better results, and a significant improvement is achieved in this study by concatenating these two descriptors for CBIR.

show abstract

“…The motif based methods derive a structure based on scanning sequence on a 2x2 grid. The motif based methods [13][14][15][16] played a key role in CBIR and in other applications. This paper derives a rule based dynamic motif matrix (RDMM) on full texton index image (FTi) (RDMM-FTi) and the feature of this descriptor is integrated with color features to derive a feature vector.…”

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

View full text Add to dashboard Cite

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.

show abstract

Face Recognition based on Cross Diagonal Complete Motif Matrix

Cited by 13 publications

References 36 publications

Classification of Textures Based on Ternary Transition Motif Matrix Features

Classification of Textures Based on Ternary Transition Motif Matrix Features

Color and cross diagonal symmetric pair co-occurrence matrix

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

Contact Info

Product

Resources

About