AUTOMATIC IMAGE CATEGORIZATION AND ANNOTATION USING K-NN FOR COREL DA- TASET

The search of an image in image database using keywords is made powerful due to automatic image annotation. In this paper, an automatic image annotation using KNearest Neighbor (K-NN) is presented. The categorization based approach is presented for annotation. Images are first segmented using k-means clustering and then processed to form feature vector. Local features are extracted from the regions of the image. The feature vectors are experimented using K-NN. Our system is validated using ten categories from the COREL images. It is observed that in multiple instance learning using K-NN with color and texture features outperforms for all type of feature vectors. KeywordsAutomatic image annotation, Color features, Texture features, KNearest Neighbor, Multiple Instance Learning. Advances in Computational Research ISSN: 0975-3273 & E-ISSN: 0975-9085, Volume 4, Issue 1, 2012 Introduction In recent years, image repositories grow exponentially with the rapid advance in information technology. A wide range of contentbased image applications have become very challenging because of the rapid expansion of representations and the volume of image data, and the complex nature of the high-level semantic meanings[1]. The term image annotation refers to the labeling of images with relevant keywords. It is very important for image retrieval and object recognition. Since manual annotation is expensive and subjective, automatic image annotation becomes the research focus of a number of researchers. These automatic image annotation systems have received intensive attention in the literature of image information retrieval since this area was started years ago, and consequently a broad range of techniques have been proposed. The algorithms used in these systems perform four tasks namely feature extraction, feature selection, training annotation system, and annotation of new images. Automatic image annotation can be done using classification based systems [2]. In recent research, an image is usually characterized by a set of instances. For example, an image can be segmented into a number of homogeneous regions [3], and one feature vector is extracted from each region to capture its visual properties, such as color, texture, shape, etc. Consequently, an image can be represented as a collection of feature vectors. Images with such representation can be categorized as follows. First a set of images with known labels are used in a learning process to design a classifier. This classifier is then used to label each image in the data set. The corresponding learning problem over bags of vectors is called Multiple-Instance Learning (MIL)[4]. In this framework, an image is referred to as a bag and a feature vector corresponds to an instance. In this paper the work carried out on COREL dataset for annotation using categorization is presented. Every image is segmented using k-means clustering to find regions. Then features of every region are extracted to form feature vector. Using K-NN multiple instance learning is carried out to classify and annotate the unlabeled image object. The results of categorization and annotation are presented. Citation: Patil M.P. and Kolhe S.R. (2012) Automatic Image Categorization and Annotation using K-NN for Corel Dataset. Advances in Computational Research, ISSN: 0975-3273 & E-ISSN: 0975-9085, Volume 4, Issue 1, pp.-108-112. Copyright: Copyright©2012 Patil M.P. and Kolhe S.R. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.