Tensorized Projection for High-Dimensional Binary Embedding

Authors

  • Weixiang Hong Nanyang Technological University
  • Jingjing Meng Nanyang Technological University
  • Junsong Yuan Nanyang Technological University

Abstract

Embedding high-dimensional visual features (d-dimensional) to binary codes (b-dimensional) has shown advantages in various vision tasks such as object recognition and image retrieval. Meanwhile, recent works have demonstrated that to fully utilize the representation power of high-dimensional features, it is critical to encode them into long binary codes rather than short ones, i.e., b ~ O(d). However, generating long binary codes involves large projection matrix and high-dimensional matrix-vector multiplication, thus is memory and computationally intensive. To tackle these problems, we propose Tensorized Projection (TP) to decompose the projection matrix using Tensor-Train (TT) format, which is a chain-like representation that allows to operate tensor in an efficient manner. As a result, TP can drastically reduce the computational complexity and memory cost. Moreover, by using the TT-format, TP can regulate the projection matrix against the risk of over-fitting, consequently, lead to better performance than using either dense projection matrix (like ITQ) or sparse projection matrix. Experimental comparisons with state-of-the-art methods over various visual tasks demonstrate both the efficiency and performance ad- vantages of our proposed TP, especially when generating high dimensional binary codes, e.g., when b ≥ d.

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Published

2018-04-25

How to Cite

Hong, W., Meng, J., & Yuan, J. (2018). Tensorized Projection for High-Dimensional Binary Embedding. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11292