Region based image retrieval using ranking concept of local binary pattern

Obulesu, A.; Kumar, Vinod; Sumalatha, L.; Niranjan, S. K.

doi:10.1109/icbdaci.2017.8070863

Cited by 5 publications

(2 citation statements)

References 40 publications

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“…Local binary patterns capture texture properties of the image [10,13,14]. In our work we have divided the image into non-overlapping blocks of size 32 X 32.…”

Section: Local Binary Patterns (Lbp)mentioning

confidence: 99%

Object based Image Retrieval with Segmentation and Extraction of Features using various Methods

2019

IJITEE

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This paper proposes Object Based Image Retrieval (OBIR) System with segmenting the objects from the images and then extracting various features from the objects. The objects are the most prominent part of an image which relates more to the human perception. First, the object present in the images is segmented by four different segmentation techniques such as K-means, Active Contours, Edge-Convex hull and Global Thresholding. Later, the color features such as Color Histogram (CH) and Color Coherence Vector (CCV), Texture feature using Local Binary Patterns (LBP) and shape feature using Histogram of Gradients (HOG) are extracted. Finally, with the usage of different segmentation and techniques mentioned above feature are extracted from objects. Results obtained are tabulated and performance study is made.

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“…Local binary patterns capture texture properties of the image [10,13,14]. In our work we have divided the image into non-overlapping blocks of size 32 X 32.…”

Section: Local Binary Patterns (Lbp)mentioning

confidence: 99%

Object based Image Retrieval with Segmentation and Extraction of Features using various Methods

2019

IJITEE

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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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Classification of Natural Images Using Machine Learning Classifiers on Graph-Based Approaches

Bhamidipati

Kumar

Kiran

2020

Advances in Computational Intelligence and Informatics

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Region based image retrieval using ranking concept of local binary pattern

Cited by 5 publications

References 40 publications

Object based Image Retrieval with Segmentation and Extraction of Features using various Methods

Object based Image Retrieval with Segmentation and Extraction of Features using various Methods

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

Classification of Natural Images Using Machine Learning Classifiers on Graph-Based Approaches

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