Content Based Image Retrieval Systems (CBIR) Using SVM Technique Matlab Project With Source Code

ABSTRACT

          Content based image retrieval utilizes representations of features that are automatically extracted from the images themselves. All most all of the current CBIR systems allow for querying by example, a technique wherein an image (or part of an image) is selected by the user as the query. The system extracts the feature of the query image, searchesthe database for images with similar features, and exhibits relevant images to the user in order of similarity to the query. In this context, content includes among other features, perceptual properties such as texture, color, shape, and spatial relationships. Many CBIR systems have been developed that compare, analyze and retrieve images based on one or more of these features. Some systems have achieved various degrees of success by combining both content based and text based retrieval. In all cases, however, there has been no definitive conclusion as to what features provide the best retrieval. In this project we present a modified SVM technique to retrieve the images similar to the query image.

         The volume of digital information is growing at an exponential rate with the steady growth of computer power, increasing access to Internet and declining cost of storage devices. Hence to effectively manage the image information, it is imperative to advance automated image learning techniques. Unlike the traditional method of text based image retrieval in which the image search is based on textual description associated with the images, Content Based Image Retrieval Systems (CBIR) retrieve image information based on the content of the image. These systems retrieve images that are semantically related to the user’s query by extracting visual contents of the image such as colour, texture, shape or any other information that can be automatically extracted from the image itself and using it as a criterion to retrieve content related images from the database. The retrieved images are then ranked according to there relevance between the query image and images in the database in proportion to a similarity measure calculated from the features .

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Mr. Roshan P. Helonde

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