A color image segmentation approach for content-based image retrieval

Özden, Mustafa; Polat, Ediz

doi:10.1016/j.patcog.2006.08.013

Cited by 64 publications

(35 citation statements)

References 11 publications

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“…Ozden.M, Polat.E in 2006 describes a new color image segmentation method based on low-level features including color, texture and spatial information [1]. The mean-shift algorithm with color and spatial information in color image segmentation is in general successful, however, in some cases, the color and spatial information are not sufficient for superior segmentation.…”

Section: Background Work and Related Methodsmentioning

confidence: 99%

Performance Analysis of Image Segmentation Algorithm

Mehra¹

2012

IOSRJEN

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Section: Background Work and Related Methodsmentioning

confidence: 99%

Performance Analysis of Image Segmentation Algorithm

Mehra¹

2012

IOSRJEN

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“…Unlike the existing histograms, the CDH counts the perceptually uniform colour difference between two points under different background with regard to colours and edge orientations, whereas the conventional methods merely count the number or frequency of pixels. The work in [9] describes a new colour image segmentation method based on low-level features including colour, texture and spatial information using mean-shift algorithm, wherein some cases were found to be not sufficient for superior segmentation with respect to colour and spatial information. The proposed work in [10] narrates three different scopes for image retrieval using various image feature proposals viz., colour co-occurrence matrix, difference between pixels of scan pattern and colour histogram.…”

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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“…The purpose of this survey is to provide an overview of the functionality of temporary image retrieval systems in terms of technical aspects. Mustafa Ozden and Ediz Polat [9] described a new colour image segmentation method based on low-level features including colour, texture and spatial information. This paper also introduces a method called wavelet frames that provide translation of invariant texture analysis.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Model for Content based Image Retrieval

Karthikeyan¹,

Aruna²

2012

IJCA

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In the recent world with the advances in multimedia technologies such as compression, display, and visualization technologies and the increased emphasis on multimedia application, the production of image information has resulted in large volume of images that need to be properly indexed for retrieval in future. Hence, there is a need for Content Based Image retrieval application which makes the retrieval process very efficient. Current systems generally make use of low level features like colour, texture, and shape. In this paper, a novel approach for generalized image retrieval based on semantic contents is presented. A combination of two feature extraction methods namely colour and edge histogram descriptor is proposed. The retrieval efficiency is computed and compared by using four methods such as k-means, colour histogram, edge histogram and sobel method. For colour, the histogram of images is computed and for edge, edge histogram descriptors (EHD) are found. For retrieval of images, a novel idea is developed based on greedy strategy to reduce computational complexity. The proposed system stores the content of database images automatically and query image's content is extracted during runtime and it is used to match against those in database. The result of the query is a set of images that are similar to the query image.

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A color image segmentation approach for content-based image retrieval

Cited by 64 publications

References 11 publications

Performance Analysis of Image Segmentation Algorithm

Performance Analysis of Image Segmentation Algorithm

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

An Efficient Model for Content based Image Retrieval

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