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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. [ABSTRACT FROM AUTHOR]

Hashing has been recognized as an efficient representation learning method to effectively handle big data due to its low computational complexity and memory cost. Most of the existing hashing methods focus on learning the low-dimensional vectorized binary features based on the high-dimensional raw vectorized features. However, the studies on how to obtain preferable binary codes from the original 2D image features for retrieval is very limited. This paper proposes a bilinear supervised discrete hashing (BSDH) method based on 2D image features which utilizes bilinear projections to binarize the image matrix features such that the intrinsic characteristics in the 2D image space are preserved in the learned binary codes. Meanwhile, the bilinear projection approximation and vectorization binary codes regression are seamlessly integrated together to formulate the final robust learning framework. Furthermore, a discrete optimization strategy is developed to alternatively update each variable for obtaining the high-quality binary codes. In addition, two 2D image features, traditional SURF-based FVLAD feature, and CNN-based AlexConv5 feature are designed for further improving the performance of the proposed BSDH method. The results of extensive experiments conducted on four benchmark datasets show that the proposed BSDH method almost outperforms all competing hashing methods with different input features by different evaluation protocols. [ABSTRACT FROM AUTHOR]