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In this paper, a novel content-based image retrieval (CBIR) method is proposed using the local sedec pattern (LScP). The local binary pattern (LBP) and the local ternary pattern (LTP), encode the relationship between the referenced pixel and its surrounding pixels, by computing gray-level difference, but in a different way. The proposed methods encode the relationship between the centre pixel and its neighbours, based on directions such as vertical, horizontal and diagonal. Calculation based on first order derivatives is used here. Second order derivative is also applied to obtain LScP. The performance of the proposed method is compared with the LTrP and other local pattern (LBP, LDP and LTP) which results are obtained using benchmark image databases viz., Corel 1000 database (DB1), Brodatz texture database (DB2). Performance of the LScP shows improvement in retrieval from 75.9%/48.7% to 86.52%/54.4% in DB1, for average precision/average recall as compared with LTrP and other local patterns. A similar comparison shows improvement from 85.30% to 91.5% in terms of average retrieval rate on database DB2.

Multimedia and digital image database management have undergone major transformation significantly in recent years in domains like data mining, medical imaging, weather forecasting and remote sensing, etc. CBIR is an important tool, useful to retrieve the required image precisely and effectively from large databases. This paper proposes the use of low level visual content features viz, mean value and colour histogram, to retrieve the colour feature and gray level co-occurrence matrix is proposed for retrieval of the texture feature. IGA is integrated with the above said low level features to derive refined results for query image matching from the database and to differentiate retrieval performance among the image features. The proposed method yields an accurate and faster retrieval from a Corel image database.

The aim of this paper is to review the current state of the art in image retrieval using meta-heuristics. Besides, this paper provides a wide survey on the technical achievements in the research area of image retrieval for business applications especially using meta-heuristics. This survey includes top 33 papers covering the research aspects of image features representation, extraction and system design. Further, based on the demand from real world applications, open research issues are identified and future research directions are suggested.