Abstract | ||
---|---|---|
Tag-based retrieval of multimedia content is a difficult problem, not only
because of the shorter length of tags associated with images and videos, but
also due to mismatch in the terminologies used by searcher and content creator.
To alleviate this problem, we propose a simple concept-driven probabilistic
model for improving text-based rich-media search. While our approach is similar
to existing topic-based retrieval and cluster-based language modeling work,
there are two important differences: (1) our proposed model considers not only
the query-generation likelihood from cluster, but explicitly accounts for the
overall "popularity" of the cluster or underlying concept, and (2) we explore
the possibility of inferring the likely concept relevant to a rich-media
content through the user-created communities that the content belongs to.
We implement two methods of concept extraction: a traditional cluster based
approach, and the proposed community based approach. We evaluate these two
techniques for how effectively they capture the intended meaning of a term from
the content creator and searcher, and their overall value in improving image
search. Our results show that concept-driven search, though simple, clearly
outperforms plain search. Among the two techniques for concept-driven search,
community-based approach is more successful, as the concepts generated from
user communities are found to be more intuitive and appealing. |
Year | Venue | Keywords |
---|---|---|
2011 | Computing Research Repository | language model,information retrieval,probabilistic model |
Field | DocType | Volume |
Data mining,Information retrieval,Computer science,Popularity,Artificial intelligence,Statistical model,Concept extraction,Language model,Machine learning | Journal | abs/1102.5 |
Citations | PageRank | References |
0 | 0.34 | 17 |
Authors | ||
4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Amruta Joshi | 1 | 187 | 8.67 |
Junghoo Cho | 2 | 3088 | 584.54 |
Dragomir Radev | 3 | 5167 | 374.13 |
Ahmed Hassan | 4 | 943 | 57.64 |