Can ambiguous words be helpful in image-understanding systems?

A semantic gap always decreases the performance of the mapping for image-to-word, which is an important task in image understanding. Even efficient learning algorithms cannot solve this problem because: (1) of a lack of coincidence between the low-level features extracted from the visual data and the high-level information translated by human, and (2) an ambiguous word may lead to a wrong interpretation between low-level and high-level information. This paper introduces a discriminative model with a ranking function that optimizes the cost between the target word and the corresponding images, while simultaneously discovering the disambiguated senses of those words that are optimal for supervised tasks. Experiments were conducted using two datasets, and results show quite a promising result when compared with existing methods.