Deep Feature Factorization for Content-Based Image Retrieval and Localization

By Edo Collins, Sabine Süsstrunk. 2019

9 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

1. Yujiu Yang
2. Ming-Hsuan Yang
3. Weihao Xia
4. Yulun Zhang
5. Jing-Hao Xue
6. Luc Van Gool
7. Bolei Zhou
8. Lei Zhang

Citations in the corpus

• GAN Inversion: A Survey (2021, methodology): “[101] use PCA, both of which seem similar to the aforementioned methods [96], [188], [194], we still do not categorize the”