https://scholars.lib.ntu.edu.tw/handle/123456789/412938
標題: | Query expansion for hash-based image object retrieval | 作者: | Kuo Y.-H. Chen K.-T. Chiang C.-H. WINSTON HSU |
關鍵字: | Locality sensitive hashing (LSH); Query expansion | 公開日期: | 2009 | 起(迄)頁: | 65-74 | 來源出版物: | 2009 ACM Multimedia Conference | 摘要: | 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. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/412938 | ISBN: | 9781605586083 | DOI: | 10.1145/1631272.1631284 |
顯示於: | 資訊工程學系 |
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