Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey <s> Introduction <s> In order to improve the retrieval accuracy of content-based image retrieval systems, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the 'semantic gap' between the visual features and the richness of human semantics. This paper attempts to provide a comprehensive survey of the recent technical achievements in high-level semantic-based image retrieval. Major recent publications are included in this survey covering different aspects of the research in this area, including low-level image feature extraction, similarity measurement, and deriving high-level semantic features. We identify five major categories of the state-of-the-art techniques in narrowing down the 'semantic gap': (1) using object ontology to define high-level concepts; (2) using machine learning methods to associate low-level features with query concepts; (3) using relevance feedback to learn users' intention; (4) generating semantic template to support high-level image retrieval; (5) fusing the evidences from HTML text and the visual content of images for WWW image retrieval. In addition, some other related issues such as image test bed and retrieval performance evaluation are also discussed. Finally, based on existing technology and the demand from real-world applications, a few promising future research directions are suggested. <s> BIB001 </s> Describing Colors, Textures and Shapes for Content Based Image Retrieval - A Survey <s> Introduction <s> The objective of the present study is to explore prioritization methods in diagnostic imaging modalities to automatically determine the contents of medical images. In this paper, we propose an efficient prioritization of brain MRI. First, the visual perception of the radiologists is adapted to identify salient regions. Then this saliency information is used as an automatic label for accurate segmentation of brain lesion to determine the scientific value of that image. The qualitative and quantitative results prove that the rankings generated by the proposed method are closer to the rankings created by radiologists. <s> BIB002
The dawn of the twenty first century brought with itself limitless opportunities and challenges. It is rightly said that 21 st century is the age of information. With the prevalent trend of capturing almost everything including text, images, music and videos in digital form in this modern age of digital computers, the huge data collections are becoming hard to handle [1] . Thus giving rise to challenges regarding their efficient storage, indexing and on-demand retrieval. The presence of complex patterns in visual data is a major hurdle in developing efficient retrieval systems for multimedia data collections. During the last decade, this challenge has been met with tremendous endeavors by researchers all around the globe. This work focuses on the notable accomplishments in the field of image indexing and retrieval systems during the last decade. Image retrieval is the process of retrieving similar looking images from large collections usually by submitting a sample image as a query [2] . The retrieval system searches for images that are visually similar to the query image and returns a list of images ranked in order of similarity to the query image [3] . Generally there are two ways for extracting relevant images from large image collections. The traditional annotations based image retrieval (ABIR) and the recent one termed as Content based image retrieval (CBIR). ABIR methods generally rely on manual association of tags to images when they are stored in some database. Then the traditional text based retrieval systems are employed to retrieve images by searching keywords specified in the text query in the tags associated with images in the dataset  . The exponential growth of the images around us is making it extremely difficult to manually associate tags with each and every image. Secondly it is very difficult to describe visual content in words. Sometimes an image can simply not be described in words BIB001 . Thirdly there exist no general rule for describing visual content in textual form. So the description by one person might never be understood by others thus making it extremely difficult for users to get effective output from such text-based image retrieval systems [1] . To avoid the need of manual annotations and to overcome some of the issues in ABIR, content based image retrieval methods were introduced in the early 90s [2] . In CBIR, low-level features extracted from image contents are used to index and search images. A feature is any characteristic of the image that can be measured. Thus features are a collection of numbers derived from image color, texture and shape of objects contained in the image as shown in figure 1. Numerous methods exist in the literature for describing these features and even newer methods are being introduced regularly by the researcher community from the academia as well as the industry. These low-level features has been extensively used for CBIR during the last two decades. However, these low-level features fail to describe the semantics of the image. This difference in associating low-level features with high level semantics is a very well-known problem termed as the "semantic gap" BIB001 . Efforts are in progress targeting the reduction of this so called semantic gap. Machine learning methods are generally applied for deriving high level semantics from the low-level features. However the limitations in current artificial intelligence and other related techniques are a major hurdle in the way of the researchers. Hence the gap still exists and image retrieval systems are plagued with it affecting their performance badly. A typical CBIR system consist of a number of modules including query specification interface, features extraction modules, features representation and indexing modules, searching modules and image retrieval and ranking modules. In addition to these, there might exist machine learning techniques integrated into the feature extraction module BIB002 for deriving high level semantic description from low-level features and a relevance feedback mechanism as illustrated in figure 2. The purpose of all of these modules, the challenges at all of these stages of image retrieval are being discussed in the subsequent sections.