We present a reformulation of the distance metric learning problem as a penalized optimization problem, with a penalty term corresponding to the von Neumann entropy of the distance metric. This formulation leads to a mapping to statistical mechanics such that the metric learning optimization problem becomes equivalent to free energy minimization. Correspondingly, our approach leads to an analytical solution of the optimization problem based on the Boltzmann distribution. The mapping established in this work suggests new approaches for dimensionality reduction and provides insights into determination of optimal parameters for the penalty term. Furthermore, we demonstrate that the metric projects the data onto direction of maximum dissimilarity with optimal and tunable separation between classes and thus the transformation can be used for high dimensional data visualization, classification, and clustering tasks. We benchmark our method against previous distance learning methods and provide an efficient implementation in an R package available to download at: https://github.com/kouroshz/fenn.

We propose an efficient method to train multiple object detectors simultaneously using a large scale image dataset. The one-vs-all approach that optimizes the boundary between positive samples from a target class and negative samples from the others has been the most standard approach for object detection. However, because this approach trains each object detector independently, the scores are not balanced between object classes. The proposed method combines ideas derived from both detection and classification in order to balance the scores across all object classes. We optimized the boundary between target classes and their “hard negative” samples, just as in detection, while simultaneously balancing the detector scores across object classes, as done in multi-class classification. We evaluated the performances on multi-class object detection using a subset of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2011 dataset and showed our method outperformed a de facto standard method.

Text is one of the simplest way to express one's idea, and an image is one of the most impactive way to do so. Therefore, if a system can synthesize an image from text without direct user manipulation, novel image synthesis applications will be opened to users without artistic skills. In such a system, which objects to synthesize will be declared in texts. However, information about positional relations and scale of objects is not much provided and must be estimated using common sense. As described in this paper, we develop a system that can automatically synthesize objects to an image, given the background image and class name of the target synthesizing object. With the inputs as the background image and keywords, images for synthesizing objects are searched automatically. Although some previously developed systems that can synthesize an image from sketches and paintings, this is the first system that can estimate the position, scale, and appearance of objects and automatically synthesize them to images without direct user input. We propose a scene context, which indicates the position, scale, and appearance of synthesizing objects. The contribution of this paper is twofold: (1) the scene context extraction method for automatic image synthesis and (2) application of automatic image synthesis using the scene context.