Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/22498
Title: Can ambiguous words be helpful in image-understanding systems?
Authors: Zhou, H
Hu, J
Lam, KM 
Keywords: Feature extraction
Image classification
Learning (artificial intelligence)
Issue Date: 2013
Publisher: IEEE
Source: 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), October 29 2013-November 1 2013, Kaohsiung, p. 1-4 How to cite?
Abstract: 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.
URI: http://hdl.handle.net/10397/22498
ISBN: 
DOI: 10.1109/APSIPA.2013.6694144
Appears in Collections:Conference Paper

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