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In this paper, we propose saliency driven image multiscale nonlinear diffusion filtering. The resulting scale space in general preserves or even enhances semantically important structures such as edges, lines, or flow-like structures in the foreground, and inhibits and smoothes clutter in the background. The image is classified using multiscale information(More)
Large variations in image background may cause partial matching and normalization problems for histogram-based representations, i.e., the histograms of the same category may have bins which are significantly different, and normalization may produce large changes in the differences between corresponding bins. In this paper, we deal with this problem by using(More)
In this paper we propose an algorithm for the recognition of three kinds of drug-taking instruments, including bongs, hookahs and spoons. A global feature Pyramid of Histograms of Orientation Gradients (PHOG) is used to represent images. PHOG is calculated by partitioning an image into increasingly fine sub-regions and concatenating the appropriately(More)
In this paper, Multi-Task Linear Dependency Modeling is proposed to distinguish drug-related webpages that contain lots of images and text. Linear Dependency Modeling exploits semantic relations between images features and text features, and Multi-Task Learning takes advantage of metadata of webpages. Meaningful information of webpages can be made use of(More)
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