Deep regional feature pooling for video matching

In this work, we study the problem of deep global descriptors for video matching with regional feature pooling. We aim to analyze the joint effect of ROI (Region of Interest) size and pooling moment on video matching performance. To this end, we propose to mathematically model the distribution of vi...

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Bibliographic Details
Published in2017 IEEE International Conference on Image Processing (ICIP) pp. 380 - 384
Main Authors Bai, Yan, Lin, Jie, Chandrasekhar, Vijay, Lou, Yihang, Wang, Shiqi, Duan, Ling-Yu, Huang, Tiejun, Kot, Alex
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2017
Subjects
Convolutional Neural Networks
Estimation
Feature extraction
Global Descriptor
Image matching
Pipelines
Pooling
Signal to noise ratio
Transform coding
Video Matching
Video Retrieval
Visualization
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Summary:In this work, we study the problem of deep global descriptors for video matching with regional feature pooling. We aim to analyze the joint effect of ROI (Region of Interest) size and pooling moment on video matching performance. To this end, we propose to mathematically model the distribution of video matching function with a pooling function nested in. Matching performance can be estimated by the separability of these class-conditional distributions between matching and non-matching pairs. Empirical studies on the challenging MPEG CDVA dataset demonstrate that performance trends are consistent with the estimation and experimental results, though the theoretical model is largely simplified compared to video matching and retrieval in practice.
ISSN:2381-8549
DOI:10.1109/ICIP.2017.8296307