2015Unpublished venueRequires access

Analytical Study of CBIR Techniques

Mohini Patil, Chitra Dhawale, P Pote

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Abstract

Content based Image retrieval (CBIR) means search the contents of the image instead of information and capture images from database as per the user requirement. Content refers to as color, shapes, textures or any other information.The image retrieval is interesting and fastest developing methodology in all fields. It iseffective and well-organized approach for retrieving the image from large scale database. CBIR is a technique to take input as query object and gives output from an image database. To build up content based image retrieval system, to improve various processes implicated in retrieval like feature extraction, Image retrieval and similarity matching techniques. In this paper surveys has been conducted on some features such as color, texture and shape retrieval of images from the database and also study to compared content based image retrieval features like Color, texture and shape for efficient and accurate image retrieval. After going through exhaustive analysis of these CBIR techniques there isvarious parameters to review the paper, some of them it is found that each technique have its own strengths and limitations.So this paper gives summarization of the different features of images with their functionality for content based image retrieval systems.

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What this paper is about

Content based Image retrieval (CBIR) means search the contents of the image instead of information and capture images from database as per the user requirement. Content refers to as color, shapes, textures or any other information.The image retrieval is interesting and fastest developing methodology in all fields. It iseffective and well-organized approach for retrieving the image from large scale database. CBIR is a technique to take input as query object and gives output from an image database. To build up content based image retrieval system, to improve various processes implicated in retrieval like feature extraction, Image retrieval and similarity matching techniques. In this paper surveys has been conducted on some features such as color, texture and shape retrieval of images from the database and also study to compared content based image retrieval features like Color, texture and shape for efficient and accurate image retrieval. After going through exhaustive analysis of these CBIR techniques there isvarious parameters to review the paper, some of them it is found that each technique have its own strengths and limitations.So this paper gives summarization of the different features of images with their functionality for content based image retrieval systems.

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Available abstract

Content based Image retrieval (CBIR) means search the contents of the image instead of information and capture images from database as per the user requirement. Content refers to as color, shapes, textures or any other information.The image retrieval is interesting and fastest developing methodology in all fields. It iseffective and well-organized approach for retrieving the image from large scale database. CBIR is a technique to take input as query object and gives output from an image database. To build up content based image retrieval system, to improve various processes implicated in retrieval like feature extraction, Image retrieval and similarity matching techniques. In this paper surveys has been conducted on some features such as color, texture and shape retrieval of images from the database and also study to compared content based image retrieval features like Color, texture and shape for efficient and accurate image retrieval. After going through exhaustive analysis of these CBIR techniques there isvarious parameters to review the paper, some of them it is found that each technique have its own strengths and limitations.So this paper gives summarization of the different features of images with their functionality for content based image retrieval systems.

Key concepts: Image retrieval, Computer science, Automatic image annotation, Automatic summarization, Content-based image retrieval, Visual Word, Image texture, Information retrieval

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