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Highlights: [•] A novel region based inverted file for indexing and retrieval of images the same way as text documents. [•] A modified probabilistic decision tree learning algorithm suitable for image annotation. [•] A novel image retrieval system based on automatic image annotation and region translation. [ABSTRACT FROM AUTHOR]

Abstract: Nowadays, more and more images are available. However, to find a required image for an ordinary user is a challenging task. Large amount of researches on image retrieval have been carried out in the past two decades. Traditionally, research in this area focuses on content based image retrieval. However, recent research shows that there is a semantic gap between content based image retrieval and image semantics understandable by humans. As a result, research in this area has shifted to bridge the semantic gap between low level image features and high level semantics. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) which extracts semantic features using machine learning techniques. In this paper, we focus on this latest development in image retrieval and provide a comprehensive survey on automatic image annotation. We analyse key aspects of the various AIA methods, including both feature extraction and semantic learning methods. Major methods are discussed and illustrated in details. We report our findings and provide future research directions in the AIA area in the conclusions [Copyright &y& Elsevier]

Abstract: Shape is one of the primary low-level image features exploited in the newly emerged content-based image retrieval (CBIR). Many shape methods exist. Among these shape methods, Fourier descriptor (FD) is one of the most widely used shape descriptors due to its simple computation, clarity and coarse to fine description capability. FD has been applied to a variety of applications, including image retrieval application. Generally, FD can be acquired in a number of ways, however, FD acquired from different ways can have different retrieval performance. In this paper, we study shape retrieval using FD. Specifically, we study different ways of acquiring FD, the number of FD features needed for general shape description and the retrieval performance of different FD. A Java client–server retrieval framework has been developed to facilitate the study. The retrieval performance of the different FD is tested using a standard shape database and a commonly used performance measurement. [Copyright &y& Elsevier]

More and more images have been generated in digital form around the world. There is a growing interest in finding images in large collections or from remote databases. In order to find an image, the image has to be described or represented by certain features. Shape is an important visual feature of an image. Searching for images using shape features has attracted much attention. There are many shape representation and description techniques in the literature. In this paper, we classify and review these important techniques. We examine implementation procedures for each technique and discuss its advantages and disadvantages. Some recent research results are also included and discussed in this paper. Finally, we identify some promising techniques for image retrieval according to standard principles. [Copyright &y& Elsevier]

Shape description is one of the key parts of image content description for image retrieval. Most of the existing shape descriptors are usually either application dependent or non-robust, making them undesirable for generic shape description. In this paper, a generic Fourier descriptor (GFD) is proposed to overcome the drawbacks of existing shape representation techniques. The proposed shape descriptor is derived by applying two-dimensional Fourier transform on a polar-raster sampled shape image. The acquired shape descriptor is application independent and robust. Experimental results show that the proposed GFD outperforms common contour-based and region-based shape descriptors. [Copyright &y& Elsevier]

Abstract: Automatic image annotation can be used to facilitate semantic search in large image databases. However, retrieval performance of the existing annotation schemes is far from the users’ expectation. In this paper, we propose a novel method to automatically annotate image through the rules generated by support vector machines and decision trees. In order to obtain the rules, we collect a set of training regions by image segmentation, feature extraction and discretization. We first employ a support vector machine as a preprocessing technique to refine the input training data and then use it to improve the rules generated by decision tree learning. The preprocessing can effectively deal with the similar regions in an image as well. Moreover, we integrate the original rules to the modified ones, so as to formulate the complete and effective annotation rules. We can translate an unknown image into text by this algorithm, and the proposed system can retrieve images queried by both images and keywords. Experiments are carried out in a standard Corel dataset and images collected from the Web to test the accuracy and robustness of the proposed system. Experimental results show the proposed algorithm can annotate and retrieve images more efficiently than traditional learning algorithms. [Copyright &y& Elsevier]