Published Date: 2018-02-08
Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)
Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.
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.