Web-based information content and its application to concept-based video retrieval

Semantic similarity between words or phrases is frequently used to find matching correlations between search queries and documents when straightforward matching of terms fails. This is particularly important for searching in visual databases, where pictures or video clips have been automatically tagged with a small set of semantic concepts based on analysis and classification of the visual content. Here, the textual description of documents is very limited, and semantic similarity based on WordNet's cognitive synonym structure, along with information content derived from term frequencies, can help to bridge the gap between an arbitrary textual query and a limited vocabulary of visual concepts. This approach, termed concept-based retrieval, has received significant attention over the last few years, and its success is highly dependent on the quality of the similarity measure used to map textual query terms to visual concepts. In this paper, we consider some issues of semantic simi...

• Type: Conference
• Year: 2008
• Where: CIVR
• Authors: Alexander Haubold, Apostol Natsev
• Keywords: CIVR 2008, Image Analysis, Semantic Similarity, Similarity Measures, Textual Query