Constructing Concept Lexica With Small Semantic Gaps

In recent years, constructing mathematical models for visual concepts by using content features, i.e., color, texture, shape, or local features, has led to the fast development of concept-based multimedia retrieval. In concept-based multimedia retrieval, defining a good lexicon of high-level concepts is the first and important step. However, which concepts should be used for data collection and model construction is still an open question. People agree that concepts that can be easily described by low-level visual features can construct a good lexicon. These concepts are called concepts with small semantic gaps. Unfortunately, there is very little research found on semantic gap analysis and on automatically choosing multimedia concepts with small semantic gaps, even though differences of semantic gaps among concepts are well worth investigating.