Semantic Correlation Based Deep Cross-Modal Hashing For Faster Retrieval
Nikita Bhatt1, Amit Ganatra2
1Nikita Bhatt, U & P U Patel Department of Computer Engineering, CSPIT, CHARUSAT, Gujarat, India.
2Dr. Amit Ganatra, U & P U Patel Department of Computer Engineering, CSPIT, CHARUSAT, Gujarat, India.
Manuscript received on 03 July 2019 | Revised Manuscript received on 08 July 2019 | Manuscript published on 30 August 2019 | PP: 258-262 | Volume-8 Issue-10, August 2019 | Retrieval Number: I8157078919/2019©BEIESP | DOI: 10.35940/ijitee.I8157.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Due to growth of multi-modal data, large amount of data is being generated. Nearest Neighbor (NN) search is used to retrieve information but it suffers when there is high-dimensional data. However Approximate Nearest Neighbor (ANN) is a searching method which is extensively used by the researchers where data is represented in form of binary code using semantic hashing. Such representation reduces the storage cost and retrieval speed. In addition, deep learning has shown good performance in information retrieval which efficiently handle scalability problem. The multi-modal data has different statistical properties so there is a need to have method which finds semantic correlation between them. In this paper, experiment is performed using correlation methods like CCA, KCCA and DCCA on NMIST dataset. NMIST dataset is multi-view dataset and result proves that DCCA outperforms over CCA and KCCA by learning representations with higher correlations. However, due to flexible requirements of users, cross-modal retrieval plays very important role which works across the modalities. Traditional cross-modal hashing techniques are based on the hand-crafted features. So performance is not satisfactory as feature learning and binary code generation is independent process. In addition, traditional cross-modal hashing techniques fail to bridge the heterogeneous gap over various modalities. So many deep-based cross-modal hashing techniques were proposed which improves the performance in comparison with non-deep cross-modal techniques. Inside the paper, we presented a comprehensive survey of hashing techniques which works across the modalities.
Keywords: Multi-Modal data, Deep CCA (DCCA), cross-modal retrieval, hashing.
Scope of the Article: Cross Layer Design and Optimization