Asymmetric Feature Maps With Application to Sketch Based Retrieval

Giorgos Tolias, Ondrej Chum; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2377-2385

Abstract


We propose a novel concept of asymmetric feature maps (AFM), which allows to evaluate multiple kernels between a query and database entries without increasing the memory requirements. To demonstrate the advantages of the AFM method, we derive a short vector image representation that, due to asymmetric feature maps, supports efficient scale and translation invariant sketch-based image retrieval. Unlike most of the short-code based retrieval systems, the proposed method provides the query localization in the retrieved image. The efficiency of the search is boosted by approximating a 2D translation search via trigonometric polynomial of scores by 1D projections. The projections are a special case of AFM. An order of magnitude speed-up is achieved compared to traditional trigonometric polynomials. The results are boosted by an image-based average query expansion, exceeding significantly the state of the art on standard benchmarks.

Related Material


[pdf] [arXiv] [poster]
[bibtex]
@InProceedings{Tolias_2017_CVPR,
author = {Tolias, Giorgos and Chum, Ondrej},
title = {Asymmetric Feature Maps With Application to Sketch Based Retrieval},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}