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      Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice

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      Computer Vision and Image Understanding

      Elsevier BV

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          Hierarchical clustering schemes.

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            From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images

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              Aggregating local image descriptors into compact codes.

              This paper addresses the problem of large-scale image search. Three constraints have to be taken into account: search accuracy, efficiency, and memory usage. We first present and evaluate different ways of aggregating local image descriptors into a vector and show that the Fisher kernel achieves better performance than the reference bag-of-visual words approach for any given vector dimension. We then jointly optimize dimensionality reduction and indexing in order to obtain a precise vector comparison as well as a compact representation. The evaluation shows that the image representation can be reduced to a few dozen bytes while preserving high accuracy. Searching a 100 million image data set takes about 250 ms on one processor core.
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                Author and article information

                Journal
                Computer Vision and Image Understanding
                Computer Vision and Image Understanding
                Elsevier BV
                10773142
                September 2016
                September 2016
                : 150
                :
                : 109-125
                Article
                10.1016/j.cviu.2016.03.013
                © 2016

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