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This paper proposes a new approach for image classification by combining pyramid match kernel(PMK) with spatial pyramid. Unlike the conventional spatial pyramid matching (SPM) approach which only uses a single-resolution feature vector to represent an image, we use a multi-resolution feature vector to represent an image for SPM. We then calculate the match scores at each resolution of SPM representation...
Understanding a scene provided by very high resolution (VHR) satellite imagery has become a more and more challenging problem. In this paper, we propose a new method for scene classification based on saliency computing of patches sampling from the VHR images. Sparse principal component analysis (sPCA) is then adopted to select the corresponding informative salient patches for image scene representation...
This paper presents a method which focus on the increase of visual quality of SR image reconstructed from input low resolution image. Similar to the framework as exploited in [1], a modified algorithm is developed which is based on sparse linear regression and iterative back projection. Different from the techniques used in [1] [6], a feature sign search algorithm [17] is used to find the relevant...
This paper presents a new method for object tracking based on global spatial correspondence with the geometric distribution of visual words. “Spatial Pyramid Histogram” - SPH is produced by partitioning the image into increasing sub-blocks and computing histograms of features found inside each sub-block. SIFT descriptors are extracted to represent the object to construct a visual dictionary. A classifier...
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