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Continuous memories for representing sets of vectors and image collections

Ahmet Iscen 1, 2
Abstract : In this thesis, we study the indexing and query expansion problems in image retrieval. The former sacrifices the accuracy for efficiency, whereas the latter takes the opposite perspective and improves accuracy with additional cost. Our proposed solutions to both problems consist of utilizing continuous representations of a set of vectors. We turn our attention to indexing first, and follow the group testing scheme. We assign each dataset vector to a group, and represent each group with a single vector representation. We propose memory vectors, whose solution is optimized under the membership test hypothesis. The optimal solution for this problem is based on Moore-Penrose pseudo-inverse, and shows superior performance compared to basic sum pooling. We also provide a data-driven approach optimizing the assignment and representation jointly. The second half of the transcript focuses on the query expansion problem, representing a set of vectors with weighted graphs. This allows us to retrieve objects that lie on the same manifold, but further away in Euclidean space. We improve the efficiency of our technique even further, creating high-dimensional diffusion embeddings offline, so that they can be compared with a simple dot product in the query time. For both problems, we provide thorough experiments and analysis in well-known image retrieval benchmarks and show the improvements achieved by proposed methods.
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Submitted on : Monday, December 11, 2017 - 10:50:19 PM
Last modification on : Friday, April 8, 2022 - 4:08:03 PM


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  • HAL Id : tel-01661319, version 1


Ahmet Iscen. Continuous memories for representing sets of vectors and image collections. Computer Vision and Pattern Recognition [cs.CV]. Université Rennes 1; Université de Rennes I, 2017. English. ⟨NNT : 2017REN1S039⟩. ⟨tel-01661319⟩



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