CNN-VWII: An Efficient Approach for Large-Scale Video Retrieval by Image Queries

Abstract

This paper aims to solve the problem of large-scale video retrieval by a query image. Firstly, we define the problem of top-$k$ image to video query. Then, we combine the merits of convolutional neural networks(CNN for short) and Bag of Visual Word(BoVW for short) module to design a model for video frames information extraction and representation. In order to meet the requirements of large-scale video retrieval, we proposed a visual weighted inverted index(VWII for short) and related algorithm to improve the efficiency and accuracy of retrieval process. Comprehensive experiments show that our proposed technique achieves substantial improvements (up to an order of magnitude speed up) over the state-of-the-art techniques with similar accuracy.

arXiv commentsubmitted to Pattern Recognition Letters

References in corpus (6)

• Multi-View Spectral Clustering via Structured Low-Rank Matrix Factorization
• Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification
• Effective Multi-Query Expansions: Collaborative Deep Networks for Robust Landmark Retrieval
• What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification
• Iterative Views Agreement: An Iterative Low-Rank based Structured Optimization Method to Multi-View Spectral Clustering
• Structured Deep Hashing with Convolutional Neural Networks for Fast Person Re-identification