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The explosive growth of the vision data motivates the recent studies on efficient data indexing methods such as locality-sensitive hashing (LSH). Most existing approaches perform hashing in an unsupervised way. In this paper we move one step forward and propose a supervised hashing method, i.e., the LAbel-regularized Max-margin Partition (LAMP) algorithm. The proposed method generates hash functions...
Several object categorization algorithms use kernel methods over multiple cues, as they offer a principled approach to combine multiple cues, and to obtain state-of-the-art performance. A general drawback of these strategies is the high computational cost during training, that prevents their application to large-scale problems. They also do not provide theoretical guarantees on their convergence rate...
A multi-bandwidth based tracking algorithm was proposed to search for the global kernel mode when the probability density has multiple peak modes. Firstly, a monotonically decreasing sequence of bandwidths was fixed according to the target scale. At each bandwidth, using mean shift to find out the maximum probability, and starting the next iteration at the previous convergence location. Finally, the...
This paper describes a novel fast mean shift algorithm based on an accelerated iteration strategy. This new method focuses on solving the problem of high calculation complexity when high data dimension or large data sets are involved in mean shift. By predicting the mean shift vector, improved method reduces the number of iteration and speed up the calculation. The application of traffic image filtering...
In this paper, we propose a new approach for image deblurring from two images, non-blurred and blurred, in different poses by exploiting the co-existing planar object in both views. We focus on the problem of aligning the corresponding image patches, which are the co-existing planar object, in both images and propose an iterative two-stage algorithm for patch alignment and kernel estimation. In the...
Snakes have been extensively used for object segmentation and tracking in computer vision and image processing applications. One of these drawbacks is that they converge slowly, since inverse matrix is computed at each iteration. We have introduced a new external force model, called mean-shift flow field (MSFF). This external force model is computed by a novel and fast mean-shift mask. This type of...
As a nonparametric statistical method, the mean shift algorithm has recently attracted much attention in the computer vision community due to its efficiency in motion tracking and clustering analysis. Its convergence rate is, however, slow around the convergence point. One way to tackle this problem is to switch the search mechanism to Newtonpsilas method which has a quadratic order of convergence...
In this paper we demonstrate that the support vector tracking (SVT) framework first proposed by Avidan is equivalent to the canonical Lucas-Kanade (LK) algorithm with a weighted Euclidean norm. From this equivalence we empirically demonstrate that in many circumstances the canonical SVT approach is unstable, and characterize these circumstances theoretically. We then propose a novel ldquononpositive...
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