Query expansion for hash-based image object retrieval

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 7...

• Type: Conference
• Year: 2009
• Where: MM
• Authors: Yin-Hsi Kuo, Kuan-Ting Chen, Chien-Hsing Chiang, Winston H. Hsu
• Keywords: Feature Points, Hash-based Method, Locality Sensitive Hashing, MM 2009, Multimedia