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ForestHash: Semantic Hashing with Shallow Random Forests and Tiny Convolutional Networks

Publication ,  Journal Article
Qiu, Q; Lezama, J; Bronstein, A; Sapiro, G
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2018

In this paper, we introduce a random forest semantic hashing scheme that embeds tiny convolutional neural networks (CNN) into shallow random forests. A binary hash code for a data point is obtained by a set of decision trees, setting ‘1’ for the visited tree leaf, and ‘0’ for the rest. We propose to first randomly group arriving classes at each tree split node into two groups, obtaining a significantly simplified two-class classification problem that can be a handled with a light-weight CNN weak learner. Code uniqueness is achieved via the random class grouping, whilst code consistency is achieved using a low-rank loss in the CNN weak learners that encourages intra-class compactness for the two random class groups. Finally, we introduce an information-theoretic approach for aggregating codes of individual trees into a single hash code, producing a near-optimal unique hash for each class. The proposed approach significantly outperforms state-of-the-art hashing methods for image retrieval tasks on large-scale public datasets, and is comparable to image classification methods while utilizing a more compact, efficient and scalable representation. This work proposes a principled and robust procedure to train and deploy in parallel an ensemble of light-weight CNNs, instead of simply going deeper.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2018

Volume

11206 LNCS

Start / End Page

442 / 459

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Qiu, Q., Lezama, J., Bronstein, A., & Sapiro, G. (2018). ForestHash: Semantic Hashing with Shallow Random Forests and Tiny Convolutional Networks. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11206 LNCS, 442–459. https://doi.org/10.1007/978-3-030-01216-8_27
Qiu, Q., J. Lezama, A. Bronstein, and G. Sapiro. “ForestHash: Semantic Hashing with Shallow Random Forests and Tiny Convolutional Networks.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11206 LNCS (January 1, 2018): 442–59. https://doi.org/10.1007/978-3-030-01216-8_27.
Qiu Q, Lezama J, Bronstein A, Sapiro G. ForestHash: Semantic Hashing with Shallow Random Forests and Tiny Convolutional Networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2018 Jan 1;11206 LNCS:442–59.
Qiu, Q., et al. “ForestHash: Semantic Hashing with Shallow Random Forests and Tiny Convolutional Networks.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11206 LNCS, Jan. 2018, pp. 442–59. Scopus, doi:10.1007/978-3-030-01216-8_27.
Qiu Q, Lezama J, Bronstein A, Sapiro G. ForestHash: Semantic Hashing with Shallow Random Forests and Tiny Convolutional Networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2018 Jan 1;11206 LNCS:442–459.

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2018

Volume

11206 LNCS

Start / End Page

442 / 459

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences