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Related Experiment Video

Updated: Apr 23, 2026

Author Spotlight: Quantifying Pain Experience – An Illustrative Approach Using the Pain Body Diagram
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Classification and Weakly Supervised Pain Localization using Multiple Segment Representation.

Karan Sikka1, Abhinav Dhall2, Marian Stewart Bartlett1

  • 1University of California San Diego, 9450 Gilman Drive # 0440, La Jolla, CA 92093, USA.

Image and Vision Computing
|September 23, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for automatic pain recognition from videos, improving accuracy by analyzing segments instead of just frames. The method effectively detects and localizes pain expressions using sequence-level data.

Keywords:
Action classificationBag of WordsBaggingBoostingEmotion classificationPainTemporal SegmentationWeakly Supervised Learning

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Area of Science:

  • Computer Vision
  • Biomedical Engineering
  • Machine Learning

Background:

  • Automatic pain recognition from videos is crucial for clinical assessment but faces challenges due to the spontaneous nature of expressions.
  • Existing systems struggle with sequence-level ground truth and unknown pain event timing and duration.

Purpose of the Study:

  • To develop a novel framework (MS-MIL) for robust automatic pain recognition from videos.
  • To address the limitations of sequence-level labeling and unknown temporal dynamics in pain expression detection.
  • To improve the accuracy of pain classification and localization in videos.

Main Methods:

  • Proposed a Multiple Segment-based Multiple Instance Learning (MS-MIL) framework.
  • Represented video sequences as bags of multiple segments using clustering or temporal scanning.
  • Utilized Bag of Words (BoW) for segment representation and Multiple Instance Learning (MIL) for weakly labeled data.

Main Results:

  • Achieved competitive results in pain classification and localization on the UNBC-McMaster Shoulder Pain dataset.
  • Demonstrated the effectiveness of segment-based analysis over frame-based methods for spontaneous expressions.
  • Showcased significant improvements on the FEEDTUM spontaneous facial expression dataset.

Conclusions:

  • The MS-MIL framework effectively handles weakly labeled video data for pain recognition.
  • Incorporating temporal dynamics through segments enhances the detection and localization of pain expressions.
  • The approach offers a significant advancement in automatic pain recognition systems.