Self-Supervised Video Hashing with Hierarchical Binary Auto-encoder (1802.02305v1)

Abstract: Existing video hash functions are built on three isolated stages: frame pooling, relaxed learning, and binarization, which have not adequately explored the temporal order of video frames in a joint binary optimization model, resulting in severe information loss. In this paper, we propose a novel unsupervised video hashing framework dubbed Self-Supervised Video Hashing (SSVH), that is able to capture the temporal nature of videos in an end-to-end learning-to-hash fashion. We specifically address two central problems: 1) how to design an encoder-decoder architecture to generate binary codes for videos; and 2) how to equip the binary codes with the ability of accurate video retrieval. We design a hierarchical binary autoencoder to model the temporal dependencies in videos with multiple granularities, and embed the videos into binary codes with less computations than the stacked architecture. Then, we encourage the binary codes to simultaneously reconstruct the visual content and neighborhood structure of the videos. Experiments on two real-world datasets (FCVID and YFCC) show that our SSVH method can significantly outperform the state-of-the-art methods and achieve the currently best performance on the task of unsupervised video retrieval.

• The paper presents a self-supervised framework that integrates a hierarchical binary auto-encoder to capture temporal dependencies in video data.
• The approach enforces neighborhood structures in the binary code space, enabling similar videos to be clustered for more accurate retrieval.
• Experimental results on FCVID and YFCC demonstrate state-of-the-art performance with improved efficiency, outlining promising directions for future video hashing research.

Overview of Self-Supervised Video Hashing with Hierarchical Binary Auto-encoder

The paper under review presents a novel approach to video hashing, entitled Self-Supervised Video Hashing (SSVH), which seeks to address inherent inefficiencies in existing video hash functions. These conventional methods often involve isolated phases of frame pooling, learning, and binarization, which overlook the temporal order of video frames and consequently suffer from information loss. SSVH is introduced as a solution to capture the temporal nature of videos in a unified learning-to-hash framework. Central to this framework are two pivotal problems: designing an encoder-decoder architecture for binary code generation and ensuring those codes enable precise video retrieval.

Hierarchical Binary Auto-encoder

The hierarchical binary auto-encoder is a key innovation of this approach. The authors propose an architecture that models temporal dependencies with multiple granularities using a hierarchical binary auto-encoder, which is less computationally demanding compared to previously stacked architectures. This encoder-decoder system has three components: forward, backward, and global hierarchical binary decoders, which work collaboratively to model temporal information during video reconstruction. This innovative design strategy allows not just for efficient generation of compact binary codes, but also for retention of detailed temporal video features.

Neighborhood Structure and Video Retrieval

Beyond mere content reconstruction, SSVH seeks to leverage neighborhood structures in the data to improve retrieval accuracy. To achieve this, the researchers develop a method to quantify similarity between video entities, enforcing the requirement that similar videos inhabit proximate positions in the binary code space. This approach significantly enhances the discriminative capacity of the hash codes, enabling more accurate retrieval operations.

Experimental Results

The effectiveness of the SSVH framework is empirically validated on two large-scale video datasets: FCVID and YFCC. The experimental results reveal that SSVH outperforms existing state-of-the-art methodologies, setting a new performance benchmark in the field of unsupervised video retrieval. Key numerical results include substantial improvements in metrics commonly used in information retrieval, demonstrating the model's aptitude at balancing computational cost and retrieval precision.

Implications and Future Directions

The implications of this work are multifaceted. Practically, SSVH offers a scalable solution for content-based video retrieval, a demand that echoes across various applications in an era distinguished by exponential growth in online video content. Theoretically, the integration of dynamic temporal modeling with self-supervised learning can open pathways to more sophisticated representation learning paradigms.

Future research could extend this framework by incorporating adaptive mechanisms for neighborhood structure, potentially grounded in reinforcement learning, that can self-tune based on retrieval feedback. Furthermore, exploration into transferability of learned hash codes across video domains could unveil novel applications and enhance cross-domain retrieval systems.

In conclusion, this paper presents a substantial advancement in the field of unsupervised video hashing, offering both a practical tool for large-scale retrieval and a theoretical framework that enriches our understanding of video representation learning.