Yin-Hsi Kuo
Yi-Hsuan Yang
ABSTRACT
In this demonstration, we present a real-time system that addresses three essential issues of large-scale image object retrieval: 1) image object retrieval-facilitating pseudo-objects in inverted indexing and novel object-level pseudo-relevance feedback for retrieval accuracy; 2) time efficiency-boosting the time efficiency and memory usage of object-level image retrieval by a novel inverted indexing structure and efficient query evaluation; 3) recall rate improvement--mining semantically relevant auxiliary visual features through visual and textual clusters in an unsupervised and scalable (i.e., MapReduce) manner. We are able to search over one-million image collection in respond to a user query in 121ms, with significantly better accuracy (+99%) than the traditional bag-of-words model.
- J. Dean et al, "Mapreduce: Simplified data processing on large clusters," OSDI, 2004. Google Scholar
Digital Library
- T. Elsayed et al, "Pairwise document similarity in large collections with mapreduce," ACL, 2008. Google ScholarDigital Library
- B. J. Frey et al, "Clustering by passing messages between data points," Science, 2007.Google Scholar
- K.-H. Lin et al, "Boosting object retrieval by estimating pseudo-objects," ICIP, 2009. Google ScholarDigital Library
- J. Sivic et al, "Video google: a text retrieval approach to object matching in videos," ICCV, 2003. Google ScholarDigital Library
- Y.-H. Yang et al, "ContextSeer: context search and recommendation at query time for shared consumer photos," ACM MM, 2008. Google ScholarDigital Library
- J. Zobel et al, "Inverted files for text search engines," ACM Computing Surveys, 2006. Google ScholarDigital Library
Index Terms
- A technical demonstration of large-scale image object retrieval by efficient query evaluation and effective auxiliary visual feature discovery
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