Bilingual Lexicon Learning Using Tagged Images via Graph Trilateral Filter-based Feature Refinement

A novel method to realize bilingual lexicon learning (BLL) using tagged images is presented in this paper. Different from existing methods that require parallel corpora, the proposed method enables extraction of semantically similar words by utilizing not such corpora but tagged images on image sharing services. The main contribution of this paper is derivation of a novel framework to refine visual features of tagged images based on graph trilateral filter-based smoothing. This enables reduction of the influence of noisy tags that are irrelevant to contents of images. As a result, accurate BLL becomes feasible by nearest neighbor search using the refined visual features.