Aseem Behl

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Many tasks in computer vision, such as action classification and object detection, require us to rank a set of samples according to their relevance to a particular visual category. The performance of such tasks is often measured in terms of the average precision (ap). Yet it is common practice to employ the support vector machine ( svm) classifier, which(More)
The problem of ranking a set of visual samples according to their relevance to a query plays an important role in computer vision. The traditional approach for ranking is to train a binary classifier such as a support vector machine (svm). Binary classifiers suffer from two main deficiencies: (i) they do not optimize a ranking-based loss function, for(More)
Learn parameters w t , ξ t by solving the following convex problem over the set of active constraints W, argmin w.ξ 1 2 ||w|| 2 + Cξ s.t. Add the most violated constraint to set of active constraints W. Algorithm 1 describes a cutting plane algorithm for estimating the parameters of an AP-SVM by solving the following problem: min w 1 2 ||w|| 2 + Cξ, (2)(More)
The problem of ranking a set of visual samples according to their relevance to a query plays an important role in computer vision. The traditional approach for ranking is to train a binary classifier such as a support vector machine (svm). Binary classifiers suffer from two main deficiencies: (i) they do not optimize a ranking-based loss function, for(More)
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