Abstract
Finding object instances from large image collections is a challenging problem with many practical applications. Recent methods inspired by text retrieval achieved good results; however a re-ranking stage based on spatial verification is still required to boost performance. To improve the effectiveness of such instance retrieval systems while avoiding the computational complexity of a re-ranking stage, we explored the geometric correlations among local features and incorporate these correlations with each individual match to form a transformation consistency in rotation and scale space. This weak geometric correlation consistency can be used to effectively eliminate inconsistent feature matches and can be applied to all candidate images at a low computational cost. Experimental results on three standard evaluation benchmarks show that the proposed approach results in a substantial performance improvement compared with recent proposed methods.
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Acknowledgment
This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under grant number SFI/12/RC/2289, as well as the financial support of the Norwegian Research Council’s iAD project under grant number 174867.
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Zhang, Z., Albatal, R., Gurrin, C., Smeaton, A.F. (2016). Instance Search with Weak Geometric Correlation Consistency. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9516. Springer, Cham. https://doi.org/10.1007/978-3-319-27671-7_19
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DOI: https://doi.org/10.1007/978-3-319-27671-7_19
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