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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]

There has been a growing interest in implementing image search engine at the semantic level. However, most existing practical systems including popular commercial image search engines like Google and Yahoo! are either text-based or a simple hybrid of texts and visual features. This paper proposes a novel image search system based on automatic image annotation. We develop a technology which learns semantic image concepts from image contents and transforms unstructured images into textual documents, so that images are indexed and retrieved in the same way as textual documents. Existing database management systems can be used to effectively manage image contents, and image search can be as efficient as text search by transforming images into textual documents through machine learning. Experiments in both the Corel dataset and real Web dataset are performed to validate our system and the results are promising. This system suggests a new combination of texts and visual features in order to achieve a semantic image search, and is expected to become a re-ranking system to the existing image search result via the Internet. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]