Video retrieval based on deep convolutional neural network
This addresses the challenge of efficient video retrieval for applications dealing with large online video collections, representing an incremental improvement over existing methods.
The paper tackles the problem of fast video retrieval by proposing a deep convolutional neural network integrated with a binary hash function and triplet loss to extract high-level semantic features, achieving superior results compared to state-of-the-art methods on two public datasets.
Recently, with the enormous growth of online videos, fast video retrieval research has received increasing attention. As an extension of image hashing techniques, traditional video hashing methods mainly depend on hand-crafted features and transform the real-valued features into binary hash codes. As videos provide far more diverse and complex visual information than images, extracting features from videos is much more challenging than that from images. Therefore, high-level semantic features to represent videos are needed rather than low-level hand-crafted methods. In this paper, a deep convolutional neural network is proposed to extract high-level semantic features and a binary hash function is then integrated into this framework to achieve an end-to-end optimization. Particularly, our approach also combines triplet loss function which preserves the relative similarity and difference of videos and classification loss function as the optimization objective. Experiments have been performed on two public datasets and the results demonstrate the superiority of our proposed method compared with other state-of-the-art video retrieval methods.