In this paper an approach to Content Based Image Retrieval (CBIR) is examined that uses Kmeans clustering for segmenting an image and then extracts global and local features using color and shape information over the extracted regions to compute a similarity measure between images. | Dominic Mai Tạp chí KHOA HỌC & CÔNG NGHỆ 64(02): 45 - 52 AN APPROACH TO CBIR USING K-MEANS CLUSTERING AND POLYGON BASED SHAPE FEATURES Dominic Mai, Toi Nguyen Van Faculty of information Technology Thai Nguyen University ABSTRACT In this paper an approach to Content Based Image Retrieval (CBIR) is examined that uses Kmeans clustering for segmenting an image and then extracts global and local features using color and shape information over the extracted regions to compute a similarity measure between images. Although color is used as main information source for creating the clusters that an image is formed of, shape factors of the separated regions will be taken into account for the retrieval process as color is heavily dependent on the lighting of a scene. A fuzzy representation of features is chosen that suits the blurry nature of image segmentation. Từ khóa: Shape information, K-means, global feature, local feature, segment an image * INTRODUCTION In 2006 over 300 million photos were uploaded to flickr, one of the biggest photo sharing communities on the internet. This number is just for illustrating the fact that the number of pictures stored in electronic databases is increasing rapidly and the task of efficiently retrieving pictures stored in such a database is becoming more and more important. Text based search techniques can only be applied if pictures have been assigned meaningful labels describing semantic entities like people, outdoor scene, etc. Unfortunately, understanding a picture in the way humans do is a very hard task that is not yet solved by automated algorithms – this is also known as the semantic gap. As most of pictures do not come labeled or inside a labeled context (. a website) automatic retrieval of images cannot be done using text retrieval techniques. Luckily, it is not necessary to semantically understand a picture for performing a satisfying retrieval on a database. In this paper an approach to Content Based Image Retrieval .
