Alexander Haubold
ABSTRACT
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 similarity measures based on Information Content (IC), and propose a way to improve them. In particular, we note that most IC-based similarity measures are derived from a small and relatively outdated corpus (the Brown corpus), which does not adequately capture the usage pattern of many contemporary terms: for example, out of more than 150,000 WordNet terms, only about 36,000 are represented. This shortcoming reflects very negatively on the coverage of typical search query terms. We therefore suggest using alternative IC corpora that are larger and better aligned with the usage of modern vocabulary. We experimentally derive two such corpora using the WWW Google search engine, and show that they provide better coverage of vocabulary, while showing comparable frequencies for Brown corpus terms. Finally, we evaluate the two proposed IC corpora in the context of a concept-based video retrieval application using the TRECVID 2005, 2006, and 2007 datasets, and we show that they increase average precision results by up to 200%.
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Index Terms
- Web-based information content and its application to concept-based video retrieval
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Reviews
Jonathan P. E. Hodgson
A concept-based index for a collection of videos can be constructed using a set of statistical detectors for a fixed set of semantic concepts. The resulting index uses only the limited number of concepts based on the original set of semantic concepts; however, the index can be enhanced by using a corpus, such as the Brown corpus, and WordNet to map a larger set of concepts onto the smaller set used to create the original index. This mapping is derived in part from measuring the information content of each concept that in turn is derived from the original corpus. The size and coverage of the corpus is therefore a critical ingredient of the process. Given the age of the Brown corpus, there are a number of deficiencies related to its use because many of the words in WordNet do not appear in the corpus. This paper proposes two methods for constructing a new assignment of information content to concepts from WordNet. In the first, a corpus is constructed by taking for each concept in WordNet the first ten documents retrieved by Google. In the second method, the number of pages found by Google for each word serves as the basis for computing the information content of the word; thus, the Google knowledge base is in some sense the corpus. These approaches are used to construct enhanced context indexes for the Text Retrieval Conference Video (TRECVID) retrieval datasets that show substantially better performance than the systems based on the Brown corpus. The effectiveness of the "pages retrieved" count strategy is a particularly striking example of the use of the Web as a resource for large-scale data. Online Computing Reviews Service
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