A technical demonstration of large-scale image object retrieval by efficient query evaluation and effective auxiliary visual feature discovery

Kuo Y.-H.Wu Y.-L.Chen K.-T.YI-HSUAN YANGChiu T.-H.WINSTON HSU2019-07-102019-07-1020109781605589336https://scholars.lib.ntu.edu.tw/handle/123456789/412948In 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. ? 2010 ACM.image graph; image object retrieval; inverted file; MapReduce; query evaluation; query expansion; visual wordsA technical demonstration of large-scale image object retrieval by efficient query evaluation and effective auxiliary visual feature discoveryconference paper10.1145/1873951.18742862-s2.0-78650971192