Large-scale image retrieval using transductive support vector machines

In this paper, we propose a new method for large-scale image retrieval by using binary hierarchical trees and transductive support vector machines (TSVMs). We create multiple hierarchical trees based on the separability of the visual object classes, and TSVM classifier is used to find the hyperplane that best separates both the labeled and unlabeled data samples at each node of the binary hierarchical trees (BHTs). Then the separating hyperplanes returned by TSVM are used to create binary codes or to reduce the dimension. We propose a novel TSVM method that is more robust to the noisy labels by interchanging the classical Hinge loss with the robust Ramp loss. Stochastic gradient based solver is used to learn TSVM classifier to ensure that the method scales well with large-scale data sets. The proposed method significantly improves the Euclidean distance metric and achieves comparable results to the state-of-the-art on CIFAR10 and MNIST data sets, and significantly outperforms the state-of-the-art hashing methods on more challenging ImageCLEF 2013, NUS-WIDE, and CIFAR100 data sets. (C) 2017 Elsevier Inc. All rights reserved.