The rapid growth of multi-modal data in recent years has driven the strong demand for retrieving semantic related data within different modalities. Therefore, cross-modal hashing has attracted extensive interest and studies due to its fast retrieval speed and good accuracy. Most of the existing cross-modal hashing models simply apply neural networks to extract the features of the original data, ignoring the unique semantic information attached to each data by the labels. In order to better capture the semantic correlation between different modal data, a novel cross-modal hashing model called deep label feature fusion hashing (DLFFH) is proposed in this article. We can effectively embed semantic label information into data features by building label networks in different modal networks for feature fusion. The fused features can more accurately capture the semantic correlation between data and bridge the semantic gap, thus improving the performance of cross-modal retrieval. In addition, we construct feature label branch and corresponding feature label loss to ensure that the generated hash codes are discriminative. Extensive experiments have been conducted on three general datasets and the results demonstrate the superiority of the proposed DLFFH which performs better than most cross-modal hashing models. 

Deep Label Feature Fusion Hashing for Cross-Modal Retrieval

Mass multimedia data with geographical information (geo-multimedia) are collected and stored on the Internet due to the wide application of location-based services (LBS). How to find the high-level semantic relationship between geo-multimedia data and construct efficient index is crucial for large-scale geo-multimedia retrieval. To combat this challenge, the paper proposes a deep cross-modal hashing framework for geo-multimedia retrieval, termed as Triplet-based Deep Cross-Modal Retrieval (TDCMR), which utilizes deep neural network and an enhanced triplet constraint to capture high-level semantics. Besides, a novel hybrid index, called TH-Quadtree, is developed by combining cross-modal binary hash codes and quadtree to support high-performance search. Extensive experiments are conducted on three common used benchmarks, and the results show the superior performance of the proposed method.

TDCMR: Triplet-Based Deep Cross-Modal Retrieval for Geo-Multimedia Data

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TDCMR: Triplet-Based Deep Cross-Modal Retrieval for Geo-Multimedia Data