|Title:||Query expansion for hash-based image object retrieval||Authors:||Kuo Y.-H.
|Keywords:||Locality sensitive hashing (LSH); Query expansion||Issue Date:||2009||Start page/Pages:||65-74||Source:||2009 ACM Multimedia Conference||Abstract:||
An efficient indexing method is essential for content-based image retrieval with the exponential growth in large-scale videos and photos. Recently, hash-based methods (e.g., locality sensitive hashing - LSH) have been shown efficient for similarity search. We extend such hash-based methods for retrieving images represented by bags of (high-dimensional) feature points. Though promising, the hash-based image object search suffers from low recall rates. To boost the hash-based search quality, we propose two novel expansion strategies - intra-expansion and inter-expansion. The former expands more target feature points similar to those in the query and the latter mines those feature points that shall co-occur with the search targets but not present in the query. We further exploit variations for the proposed methods. Experimenting in two consumer-photo benchmarks, we will show that the proposed expansion methods are complementary to each other and can collaboratively contribute up to 76.3% (average) relative improvement over the original hash-based method. Copyright 2009 ACM.
|Appears in Collections:||資訊工程學系|
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