Abstract
Instance search is a retrieval task that searches video segments or images relevant to a certain specific instance (object, person, or location). Selecting more representative visual words is a significant challenge for the problem of instance search, since spatial relations between features are leveraged in many state-of-the-art methods. However, with the popularity of mobile devices it is now feasible to adopt multiple similar photos from mobile devices as a query to extract representative visual words. This paper proposes a novel approach for mobile instance search, by spatial analysis with a few representative visual words extracted from multi-photos. We develop a scheme that applies three criteria, including BM25 with exponential IDF (EBM25), significance in multi-photos and separability to rank visual words. Then, a spatial verification method about position relations is applied to a few visual words to obtain the weight of each photo selected. In consideration of the limited bandwidth and instability of wireless channel, our approach only transmits a few visual words from mobile client to server and the number of visual words varies with bandwidth. We evaluate our approach on Oxford building dataset, and the experimental results demonstrate a notable improvement on average precision over several state-of-the-art methods including spatial coding, query expansion and multiple photos.
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Acknowledgments
This work is supported by the National Nature Science Foundation of China (grants No. 61672133, No. 61602089, and No. 61632007), and the Fundamental Research Funds for the Central Universities (grants No. ZYGX2015J058 and No. ZYGX2014Z007).
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Wang, B., Shao, J., He, C., Hu, G., Xu, X. (2017). Spatial Verification via Compact Words for Mobile Instance Search. In: Amsaleg, L., Guðmundsson, G., Gurrin, C., Jónsson, B., Satoh, S. (eds) MultiMedia Modeling. MMM 2017. Lecture Notes in Computer Science(), vol 10133. Springer, Cham. https://doi.org/10.1007/978-3-319-51814-5_30
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