Deep Feature Factorization For Content-Based Image Retrieval And Localization

State of the art content-based image retrieval algorithms owe their excellent performance to the rich semantics encoded in the deep activations of a convolutional neural network. The difference between these algorithms lies mostly in how activations are combined into a compact global image descriptor. In this paper, we propose to use deep feature factorization to achieve this goal. By factorizing CNN activations, we decompose an input image into semantic regions, represented by both spatial saliency heatmaps and basis vectors serving as descriptors for those regions. When combined to form a global image descriptor, our experiments show that DFF surpasses the state of the art in both image retrieval and localization of the region of interest within the set of retrieved images.


Published in:
2019 Ieee International Conference On Image Processing (Icip), 874-878
Presented at:
26th IEEE International Conference on Image Processing (ICIP), Taipei, TAIWAN, Sep 22-25, 2019
Year:
Jan 01 2019
Publisher:
New York, IEEE
ISSN:
1522-4880
ISBN:
978-1-5386-6249-6
Keywords:




 Record created 2020-04-17, last modified 2020-10-29


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