With the rapid development of multimedia technology and Internet, the emergence of multimedia data is vast. As a carrier of information, image has a lot of information to be mined. Facing the massive image resources, the research on automatic, effective image analysis, recognition and indexing become challenging. Image classification is an image processing method based on different characteristics reflected by the target in the image information which distinguish different types of target, is a classical topic in the field of computer vision and pattern recognition. The work of image classification is of great significance, it can help people to view and manage the image by the semantic content, greatly reduce manual annotation in some image sharing sites, help the image retrieval and so on. Image classification has some successful applications such as: search engine; trademark classification systems; family album management; digital library and so on. The main work of this paper is to design efficient, robust image representation methods based on the bag-of-words (BoW) model which can be applied successfully in the task of image classification. Firstly, we introduce the main modules of the image classification algorithms, study the function of each module, the existing problems and the solutions. Then, we focus on the study of image representation in the image classification task based on the BoW model. The main contributions of this paper include the following two aspects: First, we propose a feature dimension reduction algorithm based on semi-supervised spectral discriminant analysis. It combines the locality preserving projections and the local fisher discriminant analysis so that the global structure of the unlabeled samples can be preserved as well as the labeled samples in different classes can be separated from each other. Our method avoids the overfitting problem which limits the development of most supervised dimensionality reduction methods when the number of available labeled samples is small. We transform the optimization problem into an eigenvalue decomposition problem so that the globally optimal solution is achieved with an analytic form. This guarantees that the proposed method is computationally reliable and efficient. This algorithm helps us to obtain more robust, more simple, more discriminative image representation. Second, we propose an image representation method by considering the local feature context. Given a positio...
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