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Anchorage, AK, USA
June 23, 2008 to June 28, 2008
ISBN: 978-1-4244-2339-2
pp: 1-8
Nikhil Rasiwasia , Department of Electrical and Computer Engineering, University of California, San Diego, USA
In recent years, query-by-semantic-example (QBSE) has become a popular approach to do content based image retrieval [20, 23, 18]. QBSE extends the well established query-by-example retrieval paradigm to the semantic domain. While various authors have pointed out the benefits of QBSE, there are still various open questions with respect to this paradigm. These include a lack of precise understanding of how the overall performance depends on various different parameters of the system. In this work, we present a systematic experimental study of the QBSE framework. This can be broadly divided into three categories. First, we examine the space of low-level visual features for its effects on the retrieval performance. Second, we study the space of learned semantic concepts, herein denoted as the “semantic space”, and show that not all semantic concepts are equally informative for retrieval. Finally, we present a study of the intrinsic structure of the semantic space, by analyzing the contextual relationships between semantic concepts and show that this intrinsic structure is crucial for the performance improvements.
Nikhil Rasiwasia, "A study of query by semantic example", CVPRW, 2008, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2008, pp. 1-8, doi:10.1109/CVPRW.2008.4563046
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