| With the development of multimedia technology and computer network, content-based image retrieval (CBIR) becomes more and more important to organize, index and retrieve the massive image information in many application scenarios. Thus, CBIR has emerged as a hot topic in recent years. However, the improvement of CBIR is hindered by the well-known semantic gap between low-level visual features, e.g. color, texture, shape, and high-level semantic concepts. Automatic image annotation (AIA) is a feasible way to narrow down the semantic gap since it attempts to establish the bridge between low-level visual features and high-level semantic concepts.Aiming at the problems and the difficulties in the field of AIA, the semantic concepts of images are mined from different views, i.e. the manner of semi-supervised learning, the learning of small samples, the scheme of pseudo relevance feedback and semantic relationship based on multiple views. Since the semantic understanding of image content is addressed based on the four views, the performance of AIA can also be largely improved. The main contributions of the dissertation are as follows:(1) Automatic image annotation in a manner of semi-supervised learningThe discussion and analysis of AIA is given in this dissertation, i.e. one image is annotated by several keywords and is segmented into many regions. Therefore, the task of AIA attributes to both the problem of multiple-classification learning and multiple-instance learning (MIL). For this, the dissertation proposes that AIA is resolved in a manner of semi-supervised learning. By independently analyzing the keywords under the framework of MIL, the multiple-classification is able to be transformed into binary-classification so that the hierarchical description of semantic granularity is implemented and the intrinsic semantic concept is effectively mined. The experimental results verify the effectiveness of the proposed framework.(2) Small sample learning in automatic image annotationAlthough many improvements are made in recent researches, the problem of small samples is more and more salient in the domain of AIA, which degrades greatly the performance of image annotation. In order to focus on the problem of small samples, the MIL strategy based on minimum reference set (MRS) is investigated in this dissertation. Then, the salient instance set with the smallest size of MRS can be accurately exploited to characterize the semantic content of keywords. Since the robustness of MIL is promoted, the quality of AIA can also be increased greatly.(3) Pseudo relevance feedback oriented automatic image annotationAnalyzed from the view of data mining, the image annotation technology possesses the consistency and the complementarities with the image search technology. To overcome the difficulties in search based image annotation, e.g. lower accuracy of relevant images, more burdens on human, the dissertation attempts to integrate the scheme of pseudo relevance feedback into the task of AIA and create the pseudo relevance probability model of automatic image annotation. Hence, more reliable relevant images are explored without human's interruption and the semantic correlations among keywords are mined by the technology of textual analysis, which leads to better annotation performance.(4) Semantic relationship analysis from multiple viewsA popular technology is focused on how to build the semantic relation of relevance model based on multiple views recently. From the view of probability relevance model, it is feasible for Hidden Markov Model (HMM) to deal with the task of AIA. Under the framework of transductive support vector machine, the correspondence of image-keyword is able to be constructed effectively. Moreover, the semantic relation of keyword-keyword is correctly mined by combining the co-occurrence and the tool of WordNet. Then, the multiple-views based relevance model, i.e. image-keyword and keyword-keyword, can be built to promote the quality of AIA.
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