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EMD- uses the distance as the ground distance and significantly simplifies the original linear programming formulation of the EMD.
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In particular, for the cross-bin distance, most of the work mainly focuses on how to improve the EMD and hence many variants have been proposed. The Quadratic-Chi distances (QCS and QCN) take into account cross-bin relationships and meanwhile reduce the effect of large bins. Diffusion distance exploits the idea of diffusion process to define the difference between two histograms as a temperature field. propose the Earth Movers Distance (EMD), which is defined as the minimal cost that must be paid to transform one histogram into the other, by considering the cross-bin information. To mitigate these problems, many cross-bin distances have been proposed. These metrics, however, only account for the difference between the corresponding bins and are hence sensitive to distortions in visual descriptors as well as quantization effects. Since a histogram can be considered as a vector of probability, many metrics such as distance, chi-squared distance, and Kullback-Leibler (KL) divergence can be used directly. When the histogram representations are adopted, the choice of histogram distance metric has a great impact on the classification performance or recognition accuracy of the specific task. These make it an excellent representation method for performing classification and recognition of objects. As a result, the resulting histogram obtains some merits of the descriptors, for example, rotation-invariant, scale-invariant, and translation-invariant. For many computer vision tasks, each object of interest can be presented as a histogram by using visual descriptors, such as SIFT, SURF, GIST, and HOG. In particular, a histogram in the statistics is the frequency distribution of a set of specific measurements over discrete intervals. Histograms are frequently used tools in natural language processing and various computer vision tasks, including image retrieval, image classification, shape matching, and object recognition, to represent texture and color features or to characterize rich information in local/global regions of objects. Comparative studies with the state-of-the-art approaches on five real-world datasets verify the effectiveness of the proposed method. With the iterative projected gradient method for optimization, we naturally introduce the norm regularization into the proposed method for sparse metric learning. In our method, the margin of sample is first defined with respect to the nearest hits (nearest neighbors from the same class) and the nearest misses (nearest neighbors from the different classes), and then the simplex-preserving linear transformation is trained by maximizing the margin while minimizing the distance between each sample and its nearest hits. In this paper, we show how to learn a general form of chi-squared distance based on the nearest neighbor model. The chi-squared distance is a nonlinear metric and is widely used to compare histograms.
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Hope you are able to take this on board and improve it.Learning a proper distance metric for histogram data plays a crucial role in many computer vision tasks. There's not much excuse for not doing this now that certificates are easy to get and free: md5 checksums served over http are not at all secure.īTW Even the login form of your site appears to POST over http! So, my second suggestion would be to run your site over https. Kali does it too.Īt the moment you are doing absolutely nothing to protect your users against hijacked/tampered downloads. If you need to change keys you can just sign the new one with the old one.Īll the major distros (Debian, Ubuntu, Mint, Fedora, etc etc) sign their releases. NB For this to be meaningful it would need to be done with a reasonably long-term GPG key that is used consistently for this purpose and managed securely, with the key fingerprint published in various places, and for the developers to check the published fingerprints are correct once in a while (from internet connections other than their usual ones).
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Would you be willing to sign your iso releases? Or generate sha256/sha512 checksums and sign those instead?
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I wouldn't expect any other security-conscious person to either. I'd love to try Xiaopan but there's no way I'm going to boot from an unverified iso.