Texture and colour region separation based image retrieval using probability annular histogram and weighted similarity matching scheme

Content-based image retrieval (CBIR) uses primitive image features for retrieval of similar images from a dataset. Generally, researchers extract these visual features from the whole image. Therefore, the extracted features contain overlapped information of texture, colour, and shape features, and it is a critical challenge in the field of CBIR. This problem can be overcome by extracting the colour features from the colour as well as shape and texture features from the intensity dominant part only. In this study, the authors have proposed an iterative algorithm to separate colour and texture dominant part of the image into two different images. Here, a combination of edge maps and gradients has been used to achieve separate colour and texture images. Further, scale-invariant feature transform and 2D dual-tree complex wavelet transform has been realised to extract unique shape and texture features from the texture image. Simultaneously, a probability-based semantic centred annular histogram has been suggested to extract unique colour features from the colour image. Finally, a novel weighted distance-based feature comparison scheme has been proposed for similarity matching and retrieval. All the image retrieval experiments have been carried out on seven standard datasets and demonstrated significant improvements over other state-of-arts CBIR systems