New strategy for CBIR by combining low-level visual features with a colour descriptor

In computer vision, the analysis of image contents plays a significant role to perform intelligent tasks such as object recognition and image retrieval. These contents can be low-level visual features or colour information within an image. For content-based image retrieval (CBIR), several methods have been proposed that focus on either low-level visual features extraction or the colour information, and very few works can be seen that retrieve the images by fusing both types of contents. Consequently, this work addresses the problem of combining low-level visual features with colour information that helps to improve the retrieval accuracy of CBIR. The proposed strategy extracts the low-level visual salient features with features from accelerated segment test feature descriptor and quantises the salient keypoints into a feature vector. The colour information of the image is extracted and segmented with non-linear L*a*b* colour space and quantised into a feature vector. The similarity for both the feature vectors including visual and colour features is computed and combined together. The top-rank images are retrieved for the obtained feature vector using the distance metric. The experimental results on two standard benchmark datasets show the improved efficiency and 85% accuracy of the proposed strategy over state-of-the-art methods.