Significant region based image retrieval using curvelet transform

Manipoonchelvi, P.; Muneeswaran, K.

doi:10.1109/iconraeece.2011.6129812

Cited by 9 publications

(8 citation statements)

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“…The proposed method is a part of the ongoing work (Manipoonchelvi & Muneeswaran 2011) of the authors. The objective of the proposed work is to identify significant regions from the colour images, represent the regions using multiple features and matching regions from query and target images to retrieve relevant images from image database.…”

Section: P Manipoonchelvi and K Muneeswaranmentioning

confidence: 99%

“…The energy function is calculated using SV and PV for a homogeneous region with a specific homogeneity measure HM. Elaborate discussion on how to compute the energy function is given in Manipoonchelvi & Muneeswaran (2011).…”

Section: Significant Regionmentioning

confidence: 99%

“…In the proposed method, instead of equally splitting the image into smaller regions, only the significant regions are extracted based on the approach given in Manipoonchelvi & Muneeswaran (2011). The extracted regions represent the image content.…”

Section: Color Descriptorsmentioning

confidence: 99%

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Multi region based image retrieval system

Manipoonchelvi¹,

Muneeswaran²

2014

Sadhana

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Multimedia information retrieval systems continue to be an active research area in the world of huge and voluminous data. The paramount challenge is to translate or convert a visual query from a human and find similar images or videos in large digital collection. In this paper, a technique of region based image retrieval, a branch of Content Based Image Retrieval, is proposed. The proposed model does not need prior knowledge or full semantic understanding of image content. It identifies significant regions in an image based on feature-based attention model which mimic viewer's attention. The Curvelet Transform in combination with colour descriptors are used to represent each significant region in an image. Experimental results are analysed and compared with the state-of-the-art Region Based Image Retrieval Technique.

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Section: P Manipoonchelvi and K Muneeswaranmentioning

confidence: 99%

Section: Significant Regionmentioning

confidence: 99%

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Multi region based image retrieval system

Manipoonchelvi¹,

Muneeswaran²

2014

Sadhana

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“…To form the curvelet texture descriptor, statistical operations are applied to these coefficients. Discrete curvelet coefficients can be defined by Equation (3) (Liu et al, 2011;Manipoonchelvi and Muneeswaran, 2011):…”

Section: Discrete Curvelet Transformmentioning

confidence: 99%

A Framework for Mri Image Retrieval Using Curvelet Transform and Euclidean Distance

Rajakumar

Muttan

2013

Journal of Computer Science

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Content Based Image Retrieval (CBIR) system supports users to retrieve relevant medical images based on their features. Current content based image retrieval systems are incapable of providing exact results to the users. To address this problem a consistent feature extraction method is required for content based medical image retrieval system to extract similar features from the images. In this study discrete curvelet based feature extraction technique is proposed to retrieve similar images and the similarity distance is calculated by using Euclidean distance. The proposed method gives better precision and recall rate. Experimental results on a database of 200 MRI images.

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“…The shape of the outer boundary is considered by boundary based shape descriptors. Zernike moments descriptors is a region based shape descriptors that describe the entire region of a shape [7]. Texture information can be used for recognizing an object [8] and structural methods are used [9] to describe it.…”

Section: Introductionmentioning

confidence: 99%

An Evolutionary Stochastic Approach for Efficient Image Retrieval using Modified Particle Swarm Optimization

Heidari¹,

Chalechale²

2016

ijacsa

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Abstract-Image retrieval system as a reliable tool can help people in reaching efficient use of digital image accumulation; also finding efficient methods for the retrieval of images is important. Color and texture descriptors are two basic features in image retrieval. In this paper, an approach is employed which represents a composition of color moments and texture features to extract low-level feature of an image. By assigning equal weights for different types of features, we can't obtain good results, but by applying different weights to each feature, this problem is solved. In this work, the weights are improved using a modified Particle Swarm Optimization (PSO) method for increasing average Precision of system. In fact, a novel method based on an evolutionary approach is presented and the motivation of this work is to enhance Precision of the retrieval system with an improved PSO algorithm. The average Precision of presented method using equally weighted features and optimal weighted features is 49.85% and 54.16%, respectively. 4.31% increase in the average Precision achieved by proposed technique can achieve higher recognition accuracy, and the search result is better after using PSO.

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Significant region based image retrieval using curvelet transform

Cited by 9 publications

References 11 publications

Multi region based image retrieval system

Multi region based image retrieval system

A Framework for Mri Image Retrieval Using Curvelet Transform and Euclidean Distance

An Evolutionary Stochastic Approach for Efficient Image Retrieval using Modified Particle Swarm Optimization

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