Content based Image Retrieval Using Multi Motif Co-Occurrence Matrix

Obulesu, A.; Kumar, Vinod; Sumalatha, L.

doi:10.5815/ijigsp.2018.04.07

Cited by 14 publications

(12 citation statements)

References 27 publications

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“…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%

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Color and cross diagonal symmetric pair co-occurrence matrix

Srinivas¹

2020

IJST

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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.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Color and cross diagonal symmetric pair co-occurrence matrix

Srinivas¹

2020

IJST

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“…A compound string is generated using the six motifs, by traversing the 2 x 2 grid based on the incremental value of contrast. In Motif co-occurrence Matrix (MCM) [47] approach, initially the image is transformed into Motif index image, where each pixel is assigned with a specific motif index ranging from 0 to 5. A co-occurrence matrix is built on motif indexed image and the features derived on MCM are used for efficient CBIR.…”

Section: Related Workmentioning

confidence: 99%

Image Retrieval based Local Motif Patterns Code

Obulesu¹,

Kumar²,

Sumalatha³

2018

IJIGSP

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We present a new technique for content based image retrieval by deriving a Local motif pattern (LMP) code co-occurrence matrix (LMP-CM). This paper divides the image into 2 x 2 grids. On each 2 x 2 grid two different Peano scan motif (PSM) indexes are derived, one is initiated from top left most pixel and the other is initiated from bottom right most pixel. From these two different PSM indexes, this paper derived a unique LMP code for each 2 x 2 grid, ranges from 0 to 35. Each PSM minimizes the local gradient while traversing the 2 x 2 grid. A co-occurrence matrix is derived on LMP code and Grey level co-occurrence features are derived for efficient image retrieval. This paper is an extension of our previous MMCM approach [54]. Experimental results on popular databases reveal an improvement in retrieval rate than existing methods.

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“…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

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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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Content based Image Retrieval Using Multi Motif Co-Occurrence Matrix

Cited by 14 publications

References 27 publications

Color and cross diagonal symmetric pair co-occurrence matrix

Color and cross diagonal symmetric pair co-occurrence matrix

Image Retrieval based Local Motif Patterns Code

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

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