Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN RepresentationsReport as inadecuate

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1 LinkMedia - Creating and exploiting explicit links between multimedia fragments Inria Rennes – Bretagne Atlantique , IRISA D6 - MEDIA ET INTERACTIONS 2 Visual Recognition Group, Faculty of Electrical Engineering, Czech Technical University in Prague

Abstract : Query expansion is a popular method to improve the quality of image retrieval with both conventional and CNN representations. It has been so far limited to global image similarity. This work focuses on diffusion, a mechanism that captures the image manifold in the feature space. The diffusion is carried out on descriptors of overlapping image regions rather than on a global image descriptor like in previous approaches. An efficient off-line stage allows optional reduction in the number of stored regions. In the on-line stage, the proposed handling of unseen queries in the indexing stage removes additional computation to adjust the precomputed data. We perform diffusion through a sparse linear system solver, yielding practical query times well below one second. Experimentally, we observe a significant boost in performance of image retrieval with compact CNN descriptors on standard benchmarks, especially when the query object covers only a small part of the image. Small objects have been a common failure case of CNN-based retrieval.

Keywords : Image search Image retrieval Similarity search Computer vision

Author: Ahmet Iscen - Giorgos Tolias - Yannis Avrithis - Teddy Furon - Ondřej Chum -

Source: https://hal.archives-ouvertes.fr/


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