This paper describes BoostMap, a method for efficient nearest neighbor retrieval under computationally expensive distance measures. Database and query objects are embedded into a vector space in which distances can be measured efficiently. Each embedding is treated as a classifier that predicts for any three objects X, A, B whether X is closer to A or to B. It is shown that a linear combination of such embedding-based classifiers naturally corresponds to an embedding and a distance measure. Based on this property, the BoostMap method reduces the problem of embedding construction to the classical boosting problem of combining many weak classifiers into an optimized strong classifier. The classification accuracy of the resulting strong classifier is a direct measure of the amount of nearest neighbor structure preserved by the embedding. An important property of BoostMap is that the embedding optimization criterion is equally valid in both metric and nonmetric spaces. Performance is evaluated in databases of hand images, handwritten digits, and time series. In all cases, BoostMap significantly improves retrieval efficiency with small losses in accuracy compared to brute-force search. Moreover, BoostMap significantly outperforms existing nearest neighbor retrieval methods such as Lipschitz embeddings, FastMap, and VP-trees.

Deep learning solutions for hand pose estimation are now very reliant on comprehensive datasets covering diverse camera perspectives, lighting conditions, shapes, and pose variations. While acquiring such datasets is a challenging task, several studies circumvent this problem by exploiting synthetic data, but this does not guarantee that they will work well in real situations mainly due to the gap between the distribution of synthetic and real data. One recent popular solution to the domain shift problem is learning the mapping function between different domains through generative adversarial networks. In this study, we present a comprehensive study on effective hand pose estimation approaches, which are comprised of the leveraged generative adversarial network (GAN), providing a comprehensive training dataset with different modalities. Benefiting from GAN, these algorithms can augment data to a variety of hand shapes and poses where data manipulation is intuitively controlled and greatly realistic. Next, we present related hand pose datasets and performance comparison of some of these methods for the hand pose estimation problem. The quantitative and qualitative results indicate that the state-of-the-art hand pose estimators can be greatly improved with the aid of the training data generated by these GAN-based data augmentation methods. These methods are able to beat the baseline approaches with better visual quality and higher values in most of the metrics (PCK and ME) on both the STB and NYU datasets. Finally, in conclusion, the limitation of the current methods and future directions are discussed.