Unifying Sum and Weighted Aggregations for Efficient yet Effective Image Representation Computation

Embedding and aggregating a set of local descriptors (e.g. SIFT) into a single vector is normally used to represent images in image search. Standard aggregation operations include sum and weighted aggregations. While showing high efficiency, sum aggregation lacks discriminative power. In contrast, w...

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Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - (2018) vom: 08. Okt.
1. Verfasser: Pang, Shanmin (VerfasserIn)
Weitere Verfasser: Xue, Jianru, Zhu, Jihua, Zhua, Li, Tian, Qi
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2018
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Journal Article
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520 |a Embedding and aggregating a set of local descriptors (e.g. SIFT) into a single vector is normally used to represent images in image search. Standard aggregation operations include sum and weighted aggregations. While showing high efficiency, sum aggregation lacks discriminative power. In contrast, weighted aggregation shows promising retrieval performance but suffers extremely high time cost. In this work, we present a general mixed aggregation method that unifies sum and weighted aggregation methods. Owing to its general formulation, our method is able to balance the trade-off between retrieval quality and image representation efficiency. Additionally, to improve query performance, we propose computing multiple weighting coefficients rather than one for each to be aggregated vector by partitioning them into several components with negligible computational cost. Extensive experimental results on standard public image retrieval benchmarks demonstrate that our aggregation method achieves state-of-the-art performance while showing over ten times speedup over baselines 
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700 1 |a Zhu, Jihua  |e verfasserin  |4 aut 
700 1 |a Zhua, Li  |e verfasserin  |4 aut 
700 1 |a Tian, Qi  |e verfasserin  |4 aut 
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