Deep neural networks (DNNs) have achieved exceptional performances in many tasks, particularly, in supervised classification tasks. However, achievements with supervised classification tasks are based on large datasets with well-separated classes. Typically, real-world applications involve wild datasets that include similar classes; thus, evaluating similarities between classes and understanding relations among classes are important. To address this issue, a similarity metric, ClassSim, based on the misclassification ratios of trained DNNs is proposed herein. We conducted image recognition experiments to demonstrate that the proposed method provides better similarities compared with existing methods and is useful for classification problems. Source code including all experimental results is available at this https URL
However, a method to estimate the similarities between classes has been proposed @cite_3 @cite_4 . In that method, images are divided into patches, and features are extracted from each patch using traditional methods, such as RGB color moment. In addition, to compute the distance between classes, we must assume that the images are generated from Gaussian mixture models (GMMs). Note that the number of GMM components must be determined manually relative to the number of target classes. In addition, the distance between classes expresses an inverse relation with similarities; they are not normalized, and their absolute values are meaningless. Here, two distances are involved, i.e., parametric distance (PD), which is the quadratic distance of the means and variances of a GMM, and an approximation of KL divergence. These two methods return similar results. Here, strong assumptions and simplifications were used to treat inter-class similarities realistically.
@cite_4 @cite_3
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Gaussian mixture models (GMM) are powerful parametric tools with many applications in machine learning and computer vision. Expectation maximization (EM) is the most popular algorithm for estimating the GMM parameters. However, EM guarantees only convergence to a stationary point of the log-likelihood function, which could be arbitrarily worse than the optimal solution. Inspired by the relationship between the negative log-likelihood function and the Kullback-Leibler (KL) divergence, we propose an alternative formulation for estimating the GMM parameters using the sliced Wasserstein distance, which gives rise to a new algorithm. Specifically, we propose minimizing the sliced-Wasserstein distance between the mixture model and the data distribution with respect to the GMM parameters. In contrast to the KL-divergence, the energy landscape for the sliced-Wasserstein distance is more well-behaved and therefore more suitable for a stochastic gradient descent scheme to obtain the optimal GMM parameters. We show that our formulation results in parameter estimates that are more robust to random initializations and demonstrate that it can estimate high-dimensional data distributions more faithfully than the EM algorithm. In this paper, we propose a novel adaptive metric learning algorithm (AML) for visual saliency detection. A key observation is that the saliency of a superpixel can be estimated by the distance from the most certain foreground and background seeds. Instead of measuring distance on the Euclidean space, we present a learning method based on two complementary Mahalanobis distance metrics: 1) generic metric learning (GML) and 2) specific metric learning (SML). GML aims at the global distribution of the whole training set, while SML considers the specific structure of a single image. Considering that multiple similarity measures from different views may enhance the relevant information and alleviate the irrelevant one, we try to fuse the GML and SML together and experimentally find the combining result does work well. Different from the most existing methods which are directly based on low-level features, we devise a superpixelwise Fisher vector coding approach to better distinguish salient objects from the background. We also propose an accurate seeds selection mechanism and exploit contextual and multiscale information when constructing the final saliency map. Experimental results on various image sets show that the proposed AML performs favorably against the state-of-the-arts.