首页|Robust and discrete matrix factorization hashing for cross-modal retrieval
Robust and discrete matrix factorization hashing for cross-modal retrieval
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NSTL
Elsevier
Hashing based methods have gained great success for cross-modal similarity search, due to its fast query speed and low storage cost. However, there are some challenging problems that need to be further solved: 1) Many approaches are sensitive to noises and outliers, because pound 2 norm is utilized in the objec-tive function, the error may be amplified. 2) Most existing methods take relaxation or rounding scheme to generate binary codes, causing a large quantization loss. 3) Many supervised cross-media algorithms usually take a large n x n matrix to preserve the similarity relationship, leading to large calculation and making them unscalable. To mitigate these challenges, we develop a novel cross-media search algorithm, i.e., robust and discrete matrix factorization hashing, dubbed RDMH. The method takes a two-step strat-egy. In the first phase, the pound 2 , 1 norm is utilized to improve the robustness, which makes our model not sensitive to noises and outliers. We can learn the hash codes directly by the proposed discrete optimiza-tion method instead of relaxation scheme, avoiding the large quantization loss. Moreover, RDMH corre-lates the hash codes and semantic labels directly instead of manipulating the large similarity matrix. In the second phase, we propose an autoencoder strategy to learn the hash functions, more valuable infor-mation can be preserved and making the hash functions more powerful. Comprehensive experiments on several databases demonstrate the superior performance and efficacy of the developed RDMH. (c) 2021 Elsevier Ltd. All rights reserved.
NSTL
Elsevier
