[Paper Review] Video retrieval based on deep convolutional neural network

Yj Dong, JG Li|arXiv (Cornell University)|Dec 1, 2017

Human Pose and Action Recognition1 references3 citations

TL;DR

This paper proposes an end-to-end deep learning framework for video retrieval that combines a deep convolutional neural network (CNN) with a binary hash function for efficient similarity search. By integrating triplet loss and classification loss, the method learns high-level semantic features and compact binary codes, outperforming state-of-the-art methods on two public datasets.

ABSTRACT

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.

Motivation & Objective

• To address the challenge of efficient and accurate video retrieval in the face of growing video data.
• To overcome limitations of traditional video hashing that rely on hand-crafted features and lack semantic understanding.
• To learn high-level semantic representations from videos using deep neural networks.
• To enable end-to-end optimization of feature learning and binary hashing for improved retrieval performance.
• To enhance similarity preservation in video retrieval using triplet loss and classification loss.

Proposed method

• A deep convolutional neural network is used to extract high-level semantic features from video frames.
• A binary hash function is integrated into the network for compact, efficient representation of video features.
• The framework is trained end-to-end using a combined loss function comprising triplet loss and classification loss.
• Triplet loss preserves relative similarity and dissimilarity among videos by comparing anchor, positive, and negative samples.
• Classification loss ensures discriminative feature learning by assigning correct video labels during training.
• The model jointly optimizes feature extraction and hash code generation for improved retrieval accuracy.

Experimental results

Research questions

• RQ1 Can a deep CNN with end-to-end learning improve video retrieval performance over traditional hand-crafted feature methods?
• RQ2 How effective is the combination of triplet loss and classification loss in preserving semantic relationships in video data?
• RQ3 To what extent does binary hashing with learned features enhance retrieval efficiency and accuracy?
• RQ4 Does the proposed method outperform existing state-of-the-art video retrieval techniques on benchmark datasets?
• RQ5 How robust is the method to variations in video content and complexity?

Key findings

• The proposed method achieves superior retrieval performance compared to state-of-the-art video retrieval methods on two public datasets.
• The integration of triplet loss and classification loss significantly improves the model's ability to preserve semantic similarity among videos.
• End-to-end training of the deep CNN and binary hash function leads to more discriminative and compact video representations.
• The model demonstrates strong generalization and efficiency due to learned binary codes, enabling fast similarity search.
• Quantitative results on benchmark datasets confirm the effectiveness of the proposed framework in terms of mean average precision and retrieval accuracy.
• The method outperforms existing hashing-based approaches by leveraging deep semantic features and joint optimization.