Shape Based Image Retrieval Utilising Colour Moments and Enhanced Boundary Object Detection Technique

The need for automatic object recognition and retrieval have increased rapidly in the last decade. In content-based image retrieval (CBIR) visual cues such as colour, texture, and shape are the most prominent features used. Texture features are not considered as a significant discriminator unless it is integrated with colour features. Colour-based image retrieval uses global and/or local features has proved its ability to retrieve images with a high degree of accuracy. In contrast, shape-based retrieval is still suffering from numerous unsolved problems such as precise edge detection, overlapping objects, and high cost of feature extraction. In this paper, global colour features are utilized to discriminate unrelated images. Furthermore, a novel hybrid approach is proposed, consisting of a combination of boundary-based shape descriptor (BBSD) and region-based shape descriptor (RBSD), image retrieval. An enhanced object boundary detection (EBOD) is proposed, which uses canny edge detector to detect shape boundaries, with morphological opening to remove isolated nodes. Subsequently, morphological closing is utilized to solidify objects within the target image to enhance shape-based features representation. Finally, shape features are extracted and Euclidean distance measure with different threshold values to measure the similarity between feature vectors is adopted. Five semantic categories of WANG image database are selected to test the proposed approach. The result of experiments are promising, when compared with most common related approaches.