Archive for October 31, 2013

A COMPREHENSIVE RADIOGRAPHIC DATABASE IMAGE: RESULT AND ANALYSIS

October 31, 2013

RESULT AND ANALYSIS

In this part, several tests are performing in order to analysis the performance of histograms and here we used coral image database. To test the proposed method. Firstly RGB image converted to intensity image. Then the image were split into bins sets and the features described in section IV were calculated which is based on coherent and incoherent pixels. Followed these feature set; images were grouped in similar clusters using k-means clustering method. Histogram refining method more refines the histogram by dividing the pixels in a given container into a number of classes based on color coherence vectors. Several features are calculated using Matlab for each of the cluster and these features are further classified using the k-means clustering method.

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A COMPREHENSIVE RADIOGRAPHIC DATABASE IMAGE: RELATED WORK

October 30, 2013

RELATED WORK

Color is common and important feature in image retrieval system. Color comparison between two images would however be time consuming and difficult problem to overcome this problem they introduced a method of reducing the amount of information. One way of doing this is by quantizing the color distribution into color histograms. Using color histogram easier way for color distribution or they used histogram divide in to different classes for matching. K-means algorithm is one of the most widely used clustering algorithms in spatial clustering analysis.

It is easy and efficient. But is also having limitations: It is sensitive to the initialization. It doesn’t perform well in global searching and is easy to get into local optimization and the improved K-means is based on the classical method to make the process of optimization more dependent. Many of image retrieval applications are based on color feature and shape feature.Estimating local texture based on pixels of the intensity image and a fuzzy index to point out the presence of major colors.

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A COMPREHENSIVE RADIOGRAPHIC DATABASE IMAGE: FEATURE EXTRACTION

October 29, 2013

FEATURE EXTRACTION

Feature (content) extraction is the basis of content-based image retrieval. In sense, features may include both visual features (color, texture, shape) and text-based features (key words, annotations).

Color

Color feature is one of the most reliable and easier visual features used in image retrieval. It is robust to background complication and is independent of image size and orientation.A lot of techniques available for retrieving images on the basis of color similarity from image database. Each image included to the collection is analyzed to compute a color histogram which shows the proportion of pixels of each color within the image. The color histogram for each image is then stored in the database. Color, texture and shape information have been the primitive image descriptors in content based image retrieval systems. Economic Calendar

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A COMPREHENSIVE RADIOGRAPHIC DATABASE IMAGE: INTRODUCTION

October 28, 2013

INTRODUCTION

Content-based image retrieval has been an active research area in recent years. There has been an experimental increase in the amount of non-text based data being generated from various sources. In particular, images have been gaining popularity as an alternative and some time more viable option for information storage. Through the increase in storage and transmission abilities more visual information is being made available on-line. These need to search and well manage large volumes of Multimedia information. However, while this presents a wealth of information, it also causes a great problem in retrieving appropriate and relevant information from databases.

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