Abstract
Spatial information plays an essential role in accurate matching of local features in applications, e.g., image retrieval. Despite of previous work, it remains a challenging problem to extract appropriate spatial information. We propose an image retrieval framework based on visual phrase. By encoding the spatial information into the similarity measure of visual phrases, our approach is able to capture accurate spatial information between visual words. Furthermore, the image-specific visual phrase selection process helps to reduce large number of redundant visual phrases. We have conducted experiments on two datasets: UKbench and TRECVID, which shows that our ideas significantly improve the performance in image retrieval application.
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Chen, J., Feng, B., Xu, B. (2014). Spatial Similarity Measure of Visual Phrases for Image Retrieval. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds) MultiMedia Modeling. MMM 2014. Lecture Notes in Computer Science, vol 8326. Springer, Cham. https://doi.org/10.1007/978-3-319-04117-9_25
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DOI: https://doi.org/10.1007/978-3-319-04117-9_25
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04116-2
Online ISBN: 978-3-319-04117-9
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