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Trajectory similarity computation is a fundamental problem in various applications (e.g., transportation optimization, behavioral study). Recent researches learn trajectory representations instead of point matching to realize more accurate and efficient ...

Hash learning aims to map multimedia data into Hamming space, in which the data point is represented by low-dimensional binary codes and the similarity relationships are preserved. Despite existing hash learning methods have been effectively used in data ...

Due to the emergence of the era of big data, cross-modal learning have been applied to many research fields. As an efficient retrieval method, hash learning is widely used frequently in many cross-modal retrieval scenarios. However, most of ...

Hash learning methods is a hot topic in the field of image retrieval. Since hash learning methods translate the original datasets into binary codes, they can greatly improve the efficiency of image retrieval. However, the traditional methods cannot well ...

Cross-modality retrieval has been widely studied, which aims to search images as response to text queries or vice versa. When faced with large-scale dataset, cross-modality hashing serves as an efficient and effective solution, which learns binary codes ...

The prevalence of mobile cameras and social media sharing services have drastically increased the volume of photos and videos, which pose a new challenge for developing efficient retrieval, mining, and visualization approaches. Meanwhile, the mobile ...