With the permeation of Web 2.0, more users used semantic tags to annotate the images created by themselves or others, and share these images on image sharing websites such as Flickr, Picasa. The explosive growth of these image resources information brings a huge challenge to image index and retrieval problem. Thus, fast and effective automatic image annotation technology has become a hot issue in current research. Image semantic parsing is a kind of fine-grained image annotation. The goal is not only pointing out "what does the image have", but also "where they are". The result is to project the semantic labels into corresponding regions. Although there are many different theories and algorithms which have been proposed, most of them rely on accurate labeled training data, that is, training images with pixel-level groundtruth labeled by human. However, high-quality manual delineations are not only labor-intensive and time-consuming to obtain, but also intrinsically ambiguous. This situation is becoming more conspicuous when meeting the demand for processing large scale of visual data. Fortunately, with the popularity of image sharing websites, many images with social tags in which the raw correspondences between images and labels are available. If these images can be directly used to assist image semantic parsing, the performance will be grearly improved. Nonetheless, how to propagate the image-level labels to regions, is a difficult issue. In addition, web images usually have noisy labels. Modeling the noisy data is also a huge chanllenge. Above all, to alleviate the dependence of fine-grained labeled data and use the easily available web images to perform image understanding task, this thesis focus on the task of weakly-supervised web image semantic parsing. And we also give solutions to deal with the noisy web images. Weakly-supervised means that only image-level labels are available. The main contributions are summarized as follows. 1.We formulate the image semantic segmentation problem as a weakly-supervised clustering method. To simultaneously maximize the consitency within the same cluster and seperabilities among different clusters, the spectral clustering and discriminative clustering are combined together. The label indicator function obtained from spectral clustering is used to guide the discriminative clustering process, by which the latent structure exist among features can be learned and discrimnative features can also be selected for ea...
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