Towards Category Based Large-Scale Image Retrieval Using Transductive Support Vector Machines

Abstract

In this study, we use transductive learning and binary hierarchical trees to create compact binary hashing codes for large-scale image retrieval applications. We create multiple hierarchical trees based on the separability of the visual object classes by random selection, and the transductive support vector machine (TSVM) classifier is used to separate 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. 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 improves the Euclidean distance metric and achieves comparable results to the state-of-art on CIFAR10 and MNIST data sets and significantly outperforms the state-of-art hashing methods on NUS-WIDE dataset.