Vol.6 (2016) No. 6 ISSN: 2088-5334 Quantization Selection of Colour Histogram Bins to Categorize the Colour Appearance of Landscape Paintings for Image Retrieval Aniza Othman # , Tengku Siti Meriam Tengku Wook * , Shereena M. Arif 1 # Department of Interactive Media, Universiti Teknikal Malaysia Melaka, Durian Tunggal, 76100, Melaka, Malaysia E-mail: aniza@utem.edu.my * Research Center for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia,43600 Bangi, Selangor, Malaysia E-mail: tsmeriam@ukm.edu.my 1 Information Technology Department, Faculty of Computing and Information Technology in Rabigh, King Abdullah University, Saudi Arabia E-mail: shereena.arif@gmail.com Abstract— 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 the Harmonic 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. Keywords— colour concept; colour appearance feature vector; image classification; CIELab colour model I. INTRODUCTION Recently, designing a search image mechanism based on user requirements has become an important and critical challenge [1]-[4]. Image Retrieval (IR) from a digital library or a database can be done using text description query or image query depending on how the applications of those retrieval systems are. Images in a database have to be indexed for IR activities such as searching and browsing purposes. Two methods commonly used to index the image are first, by manual annotation or text description and second, by using image visual content. Indexing images manually using text descriptions has shown a lack of the user's satisfaction due to human subjectivity and creativity. In addition, this method also can be time-consuming and inconsistent. To overcome those limitations, indexing method using image visual content such as colours, shape, and texture are preferred as it can be done automatically to each image in the digital libraries and provide a better set of relevant results [5]. Visual content or visual features of an image are characteristics that give meaning to an image. Colour feature is always said to be the easiest and strongest feature captured by human eyes [6]. Colour feature extracted from an image contains colour information. This information can be captured and its distribution can be represented. Examples of colour representation are colour histogram, colour correlogram, colour moments, and colour coherence vector [7]. It has been observed that the colour histogram representation can suit many content-based image retrieval applications, image classification, measuring the similarity between images and used effectively for image indexing as well [8]-[10]. 930