Content Based Image Retrieval (CBIR) is a technique that enables a user to extract an image based on a query, from a database containing a large amount of images. A very fundamental issue in designing a content based image retrieval system is to select the image features that best represent the image contents in a database. In this paper, our proposed method mainly concentrated on database classification and efficient image representation. We present a method for content based image retrieval based on support vector machine classifier. In this method the feature extraction was done based on the colour string coding and string comparison. We succeed in transferring the images retrieval problem to strings comparison. Thus the computational complexity is decreases obviously. The image database used in our experiment contains 1800 colour images from Corel photo galleries. This CBIR approach has significantly increased the accuracy in obtaining results for image retrieval. © 2015 The Authors. Published by Elsevier B.V. Peer-review under responsibility of scientific committee of 2nd International Symposium on Big Data and Cloud Computing (ISBCC'15).

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Content Based Image Retrieval (CBIR) has been a challenging area of research for more than a decade. In this area of research, selection of features to represent an image in the database is still an unresolved issue. Unfortunately, the existing solutions regarding the problem are only focusing on the relevance feedback techniques to improve the count of similar images related to a query from the raw image database. These approaches are inefficient and inaccurate to query the image. We propose a new efficient technique to solve these problems by exploiting a new strategy called preprocessing image database using k-means clustering and genetic algorithm. This technique utilizes several features of the image, such as color, edge density, boolean edge density and histogram information as the input of retrieval. Furthermore, several performance metrics, such as confusion matrix, precision graph and F-measures, have also been used in measuring the accuracy of the proposed technique.The experiment results show that the clustering purity in more than half of the clusters has been above 90 percent purity.

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