Research Article Open Access

Content Based Image Retrieval using Statistical Feature and Shape Extraction

Abstract

Content based image retrieval is based on automated matching of the features of the query image with that of image database through some image similarity evaluation. This can be done by extracting a useful feature from the query image as well as database image. The features like histogram, texture and shape extraction plays very vital role in the proper image retrieval. Here we have implemented a method of image retrieval using the histogram, Statistical and Shape features. In proposed method has two phases. In the first phase, we computed statistical or texture feature from each non uniform histogram bin for each RGB color components. Then we calculated similarity between the query image and database image using Euclidean distance to retrieve first round of result which is denoted as result A. In the second phase, we use Hu moments to extract the feature from result A and again measure the similarity between the query image and resulting image and in the final result most similar images are shown. The Proposed method is tested on Wang image database containing 1000 general – purpose color image and results are shown in terms of precision and recall. The performance of the proposed method is compared with the existing systems in the literature.

Piyush Kothyari and Shriprakash Dwivedi

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