As “big data” transforms the way we solve computer vision problems, the question of how we can efficiently leverage large labelled databases becomes increasingly important. High-dimensional features, such as the convolutional neural network activations that drive many leading recognition frameworks, pose particular challenges for efficient retrieval. We present a novel method for learning compact binary codes in which the conventional dense projection matrix is replaced with a discriminatively-trained sparse projection matrix. The proposed method achieves two to three times faster encoding than modern dense binary encoding methods, while obtaining comparable retrieval accuracy, on SUN RGB-D, AwA, and ImageNet datasets. The method is also more accurate than unsupervised high-dimensional binary encoding methods at similar encoding speeds.
CITATION STYLE
Tung, F., & Little, J. J. (2017). SSP: Supervised sparse projections for large-scale retrieval in high dimensions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10111 LNCS, pp. 338–352). Springer Verlag. https://doi.org/10.1007/978-3-319-54181-5_22
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