Image Retrieval Using Colour Co-occurrence Histograms

Content-based image retrieval (CBIR) has been intensively studied recent years due to its importance in various database management and computer vision applications. Searching by an image example that allows to retrieve a given image or similar images from a large image collection is one of the most challenging CBIR problems today. The paper proposes and investigates a new algorithm for a partial solution of this problem. The algorithm uses combined colour – texture features to find out whether an image contains spatially homogeneous colour textured regions similar to the given example (training image). First, quantization of the HSV colour space focuses only on the colours to be found during the search. Secondly, similarity between characteristic normalised colour co-occurrence histograms (nCCHs) in the moving windows over the image and the like training nCCHs is measured to detect the desired regions. Finally, the frequency distributions of the similarity values are compared to rank the images in the database in their similarity to the training image. Our experiments show that the proposed algorithm effectively retrieves images containing the desired textures.