Convolutional Neural Network-Assisted Adaptive Sampling for Sparse Feature Detection in Image and Video Data

Geet Lahoti, Chitta Ranjan, Jialei Chen, Hao Yan, Chuck Zhang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In this article, we propose a feature detection approach that employs an adaptive sampling technique coupled with a convolutional neural network (CNN) model, to detect sparse features of interest in high-dimensional input data. Adaptive sampling criterion smartly explores the high-dimensional input and exploits the regions of interest. The CNN model determines the likelihood of the presence of the desired features, which guides the exploitation component of the sampling strategy. The effectiveness of the approach is illustrated using case studies, where emotions in a candidate's interview video are detected for evaluation purpose and anomalies in a product's image are extracted for quality control. The approach reduces evaluation time and minimizes amount of input data to be accessed and processed while effectively identifying desired sparse features.

Original languageEnglish (US)
Pages (from-to)45-57
Number of pages13
JournalIEEE Intelligent Systems
Volume38
Issue number1
DOIs
StatePublished - Jan 1 2023

Keywords

  • Adaptive Sampling
  • Convolutional Neural Network
  • Image Feature Detection
  • Video Feature Detection

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications

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