An efficient content based image retrieval using enhanced multi-trend structure descriptor

Abstract

This paper contributes a new variant of multi-trend structure descriptor (MTSD) for efficient content based image retrieval. The proposed variant of MTSD encodes color/edge orientation/texture quantized values versus orientation of equal, small and large trends instead of color/edge orientation/texture quantized values versus equal, small and large trends. In addition, it also encodes color/edge orientation/texture quantized values versus average location of distribution of pixel values for equal, small and large trends at each orientation. To reduce the time cost of the proposed variant of MTSD with the preservation of its accuracy, the image is decomposed into fine level using discrete Haar wavelet transform and the fine level for the decomposition of an image is determined empirically. Comprehensive experiments are conducted using the benchmark Corel-1k, Corel-5k, Corel-10k, Caltech-101, LIDC-IDRI-CT, VIA/I-ELCAP-CT and OASIS-MRI image datasets and the results evident that the proposed variant of MTSD achieves the state-of-the-art performance for natural, textural and biomedical image retrieval. Precision and recall are the measures used to measure the accuracy. Euclidean similarity measure is used to calculate the similarity information between query and target images.