Kuo Y.-H.Chen K.-T.Chiang C.-H.WINSTON HSU2019-07-102019-07-1020099781605586083https://scholars.lib.ntu.edu.tw/handle/123456789/412938An 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.Locality sensitive hashing (LSH); Query expansionconference paper10.1145/1631272.16312842-s2.0-72449144858