Xianjun Zhang

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Image classification is one of the important parts of digital image processing. We propose a novel feature space-based image classification method by combining manifold learning and mixture model. In this paper, the process of image classification can be viewed as two parts: a coarse-grained classification and a fine-grained classification. In the(More)
Wavelet-based representations of images are superior to traditional block-based methods due to its unique joint space-frequency characteristics, particularly for low bit rate coding. However, in wavelet-based image coding, the quantization errors I the high frequency subbands where the sharp structures are present result in various distortions such as(More)
In this paper, we present a progressive image reconstruction scheme based on the semantically scalable multi-scale edge representation of images, with the resolution and visual quality scalable to various bitrate requirements. In the multi-scale edge representation an image is decomposed into its multi-scale primal sketch and the background where the(More)
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