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Study on 2D Feature-Based Hash Learning
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DOI：10.1109/TMM.2019.2940875.png)
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摘要
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Hashing is an important topic in image processing, as it can help save a considerable amount of storage and computational cost. Recently, inspired by 2D strategies employed in other areas of image processing, such as feature extraction, some 2D-based hashing methods were proposed. Related papers have shown that these methods may have better image retrieval performance in terms of both effectiveness and efficiency. However, the difference in the retrieval performances of hashing methods resulting from different forms of input (1D or 2D) has not been previously studied. Whether the widely used bilinear strategy in 2D-based hashing can truly help improve the retrieval precision has not been investigated in existing research. In this paper, we conduct a comparison study on 1D and 2D feature-based hashing methods and attempt to theoretically and experimentally analyse the differences in using 1D and 2D features in hashing. Furthermore, two new hashing methods are proposed for conducting the comparison experiments. Through a comprehensive study, we obtain three main conclusions in this paper: 1) Linear projection on 1D features and bilinear projection on 2D features are essentially the same. 2) 2D-based hashing methods are obviously more efficient than 1D-based methods for analysing high-dimensional input features. 3) 2D-based hashing methods show generally better performance for solving small sample size problems.
Keyword:
Two dimensional displays
Feature extraction
Binary codes
Image retrieval
Matrix converters
Sparse matrices
learning to hash
2D image features
bilinear projection
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