Quantization Selection of Colour Histogram Bins to Categorize the Colour Appearance of Landscape Paintings for Image Retrieval

In the world of today, most images are digitized and kept in digital libraries for better organization and management. With the growth of information and communication technology, collection holders such as museums or cultural institutions have been increasingly interested in making their collections available anytime and anywhere for any Image Retrieval (IR) activities such as browsing and searching. In a colour image retrieval application, images retrieved by users are accomplished according to their specifications on what they want or acquire, which could be based upon so many concepts. We suggest an  approach to categorize the colour appearances of whole scene landscape painting images based on human colour perception. The colour features in the image are represented using a colour histogram. We then find  the suitable quantization bins that can be used to generate optimum colour histograms for all categories of colour appearances, which is selected based on theHarmonic Mean of the precision and recall,  also known as F-Score percentage higher saturated value. Colour appearance attributes in the CIELab colour model (L-Lightness, a and b are colour-opponent dimension) are used to generate colour appearance feature vectors namely the saturation metric, lightness metric and multicoloured metric. For the categorizations, we use the Nearest Neighbour (NN) method to detect the classes by using the predefined colour appearance descriptor measures and the pre-set thresholds.  The experimental results show that the quantization of CIELab colour model into 11 uniformly bins for each component had achieved the optimum result for all colour appearances categories.