Framework for image retrieval using machine learning and statistical similarity matching techniques

The aim of this study is to take advantage of both shape and texture properties of image to improve the performance of image indexing and retrieval algorithm. Further, a framework for partitioning image into non-overlapping tiles of different sizes, which results in higher retrieval efficiency, is presented. In the new approach, the image is divided into different regions (tiles). Then, the energy and standard deviation of Hartley transform coefficients of each tile, which serve as the local descriptors of texture, are extracted as sub-features. Next, invariant moments of edge image are used to record the shape features. The shape features and combination of sub-features of texture provide a robust feature set for image retrieval. The most similar highest priority (MSHP) principle is used for matching of textural features and Canberra distance is utilised for shape features matching. The retrieved image is the image which has less MSHP and Canberra distance from the query image. The proposed method is evaluated on three different image sets, which contain about 17 000 images. The experimental results indicate that the proposed method achieves higher retrieval accuracy than several previously presented schemes, whereas the computational complexity and processing time of the new method are less than those of other approaches.