Abstract
High feature vector dimension quietly remained a curse element for Content Based Image Retrieval (CBIR) system which eventually degrades its efficiency while indexing similar images from database. This paper proposes CBIR system using Gray Scale Weighted Average technique for reducing the feature vector dimension. The proposed method is more suitable for color and texture image feature analysis as compared to color weighted average method as illustrated in literature review. To prove the effectiveness of retrieval system, two standard benchmark dataset namely, Wang and Amsterdam Library of Texture Images (A LOT) for color and texture have been selected to evaluate the system retrieval accuracies as well as efficiencies generated by each method. For the purpose of image similarity, Euclidean distance has been employed which matches query image feature vector with image database feature vectors. The experimental results generated by two methods showed that overall performance of the proposed method is relatively better in terms of average precision, average recall and its average retrieval time.
Highlights
Due to over increasing growth in digital images on the web their storage management as well as retrieval is becoming a challenging task
Traditional Text Based Image Retrieval (TBIR) system is an inadequate method to retrieve similar images from the databases, because it is difficult to extract visual features with the aid of textual information. To address this problem the Content Based Image Retrieval System (CBIR) was introduced in early 1990, since this area has been most extensively researched for images
Image retrieval using fusion of cell color histogram (CCH) and color coherence vector (CCV) was presented by Salami and Boucheham [12], in their method classification of coherent and non-coherent pixels have been performed using CCV and CCH was implemented for color distinction
Summary
Due to over increasing growth in digital images on the web their storage management as well as retrieval is becoming a challenging task. Traditional Text Based Image Retrieval (TBIR) system is an inadequate method to retrieve similar images from the databases, because it is difficult to extract visual features with the aid of textual information. To address this problem the Content Based Image Retrieval System (CBIR) was introduced in early 1990, since this area has been most extensively researched for images. Until now many image retrieval methods have been proposed by many authors where they have presented multilevel feature extraction using color, texture and shape to improve the accuracy of image retrieval process. The goal of this paper is to introduce one dimensional color feature vector for image retrieval using gray scale weighted average method.
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