A multiresolution approach for rotation invariant texture image retrieval with orthogonal polynomials model

Krishnamoorthi, R.; Devi, S. Sathiya

doi:10.1016/j.jvcir.2011.07.011

Cited by 26 publications

(14 citation statements)

References 19 publications

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“…A comparative study is performed with the existing methods such as Orthogonal polynomial [19], Gabor wavelet transform [20] and the Contourlet transform [27] methods and Statistical distributional approach [28], and the obtained results are presented in Table 1. The results reveal that the proposed method outperforms the existing methods.…”

Section: Image Database Design and Experimental Resultsmentioning

confidence: 99%

“…The proposed system avoids this problem, because it uses the global distributional differences of both query and target images; in the case of structured images, these features are extracted from the shapes in both query and target images, and those are compared shape-wise, it compares the number of shapes between the images. The orthogonal polynomial based method [19] retrieve only textured images with grayscale, and the Gabor features based method [20] retrieves only the textured images in both color and gray-scale. The proposed system retrieves both textured and structured color images, and it is robust for scaled and rotated images.…”

Section: Discussionmentioning

confidence: 99%

“…In the proposed technique, mean and variance (first and second moments of the sample points) are used as representatives of both query and target images. The methods proposed in [19,20] retrieve only the texture images with intensity values ranging from 0-255, i.e. gray-scale images.…”

Section: Introductionmentioning

confidence: 99%

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Color Image Retrieval Based on Non-Parametric Statistical Tests of Hypothesis

Shekhar¹,

Seetharaman

2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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A novel method for color image retrieval, based on statistical non-parametric tests such as twosample Wald Test for equality of variance and Man-Whitney U test, is proposed in this paper. The proposed method tests the deviation, i.e. distance in terms of variance between the query and target images; if the images pass the test, then it is proceeded to test the spectrum of energy, i.e. distance between the mean values of the two images; otherwise, the test is dropped. If the query and target images pass the tests then it is inferred that the two images belong to the

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Section: Image Database Design and Experimental Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Color Image Retrieval Based on Non-Parametric Statistical Tests of Hypothesis

Shekhar¹,

Seetharaman

2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…In [7], a simple and yet efficient CBIR based on orthogonal polynomials model is presented. This model is built with a set of carefully selected orthogonal polynomials and is used to extract the low level texture features present in the image under analysis.…”

Section: Related Workmentioning

confidence: 99%

Utilising Fuzzy Rough Set Based on Mutual Information Decreasing Method for Feature Reduction in an Image Retrieval System

Lotfabadi

Shiratuddin

Wong

2014

Lecture Notes in Electrical Engineering

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Content-Based Image Retrieval (CBIR) system has become a focus of research in the area of image processing and machine vision. General CBIR system automatically index and retrieve images with visual features such as colour, texture and shape. However, current research found that there is a significant gap between visual features and semantic features used by humans to describe images. In order to bridge the semantic gap, some researchers have proposed methods for managing and decreasing image features, and extract useful features from a feature vector. This paper presents an image retrieval system utilising fuzzy rough set based on mutual information decreasing method and the Support Vector Machine (SVM) classifier. The system has training and testing phases. In order to reduce the semantic gap, the propose retrieval system used relevance feedback to improve the retrieval performance. This paper also compared the proposed method with other traditional retrieval systems that use PCA, kernel PCA, Isomap and MVU for their feature reduction method. Experiments are carried out using a standard Corel dataset to test the accuracy and robustness of the proposed system. The experiment results show the propose method can retrieve images more efficiently than the traditional methods. The use of fuzzy rough set based on mutual information decreasing method, SVM and relevance feedback ensures that the propose image retrieval system produces results which are highly relevant to the content of an image query.

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“…A new CBIR approach for biometric security based on colour, texture and shape features e controlled by fuzzy heuristics is described in [12]. Multi-resolution approach for retrieval of rotation invariant texture image is presented in [13,14].…”

Section: Previous Workmentioning

confidence: 99%

A Content Based Approach to Medical X-Ray Image Retrieval using Texture Features

Ganesan

Subashini

2014

IJCT

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Of late, the amount of digital X-ray images that are produced in hospitals is increasing incredibly fast. Efficient storing, processing and retrieving of X-ray images have thus become an important research topic. With the exponential need that arises in the search for the clinically relevant and visually similar medical images over a vast database, the arena of digital imaging techniques is forced to provide a potential and path-breaking methodology in the midst of technical advancements so as to give the best match in accordance to the user's query image. CBIR helps doctors to compare X-rays of their current patients with images from similar cases and they could also use these images as queries to find the similar entries in the X-ray database. This paper focuses on six different classes of X-ray images, viz. chest, skull, foot, spine, pelvic and palm for efficient image retrieval. Initially the various X-rays are automatically classified into the six-different classes using BPNN and SVM as classifiers and GLCM co-efficient as features for classification. Indexing is done to make the retrieval fast and retrieval of similar images is based on the city block distance.Key Words: Content based image retrieval; back propagation neural network; support vector machine; gray level cooccurrence matrix and city block distance method.

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A multiresolution approach for rotation invariant texture image retrieval with orthogonal polynomials model

Cited by 26 publications

References 19 publications

Color Image Retrieval Based on Non-Parametric Statistical Tests of Hypothesis

Color Image Retrieval Based on Non-Parametric Statistical Tests of Hypothesis

Utilising Fuzzy Rough Set Based on Mutual Information Decreasing Method for Feature Reduction in an Image Retrieval System

A Content Based Approach to Medical X-Ray Image Retrieval using Texture Features

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