.png){kind=icon}
.png){kind=icon}
Return
Nonlinear Sparse Hashing
.png){kind=icon}
PRE
AI
DOI：10.1109/TMM.2017.2705918.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
To facilitate fast similarity search, this paper proposes to encode the nonlinear similarity and image structure as compact binary codes. Rather than adopting single matrix as projection in the literature, we employ a nonlinear transformation in the form of multilayer neural network to generate binary codes to capture the local structure between data samples. Specifically, we train the network such that the quantization loss is minimized and the variance over all bits is maximized. In addition, we capture the salient structure of image samples at the abstract level with sparsity constraint and inherit the generalization power to unseen samples. Furthermore, we incorporate the supervisory label information into the learning procedure to take advantage of the manual label. To obtain the desired binary codes and the parameterized nonlinear transformation, we optimize the formulated objective problem over each variablewith an iterative alternatingmethod. To validate the efficacy of the proposed hashing approach, we conduct experiments on three widely used datasets, namely CIFAR10, MNIST, and SUN397, by comparing with several recent proposed hashing methods.
Keywords:
Hashing
informative encoding
nonlinear transformation
sparse
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.7
Papers:
4.5K
Citations:
2.4W