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Updated: Jun 23, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Semi-automatically labeling objects in images.

Wen Wu1, Jie Yang

  • 1Language Technologies Institute, School of Computer Science at Carnegie Mellon University, Pittsburgh, PA 15213, USA. wenwu@cs.cmu.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|April 29, 2009
PubMed
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Manually labeling images for AI training is slow. SmartLabel-2 offers a semi-automatic solution, significantly reducing human input for accurate object detection and contour extraction with minimal data.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Accurate object labeling in images is essential for training machine learning models in applications like object detection and image retrieval.
  • Manual image annotation is a labor-intensive and time-consuming process, hindering the development of large-scale visual learning systems.

Purpose of the Study:

  • To develop efficient semi-automatic methods for object labeling in images, reducing the need for extensive manual annotation.
  • To enhance existing semi-automatic labeling techniques by addressing limitations such as the need for negative data and improving contour extraction accuracy.

Main Methods:

  • A graph-based semi-supervised learning algorithm is employed for iterative object labeling.
  • The SmartLabel method minimizes a quadratic energy function, incorporating relevance feedback to address data scarcity.

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End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
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Published on: December 15, 2023

  • SmartLabel-2 improves upon SmartLabel by automatically sampling negative examples, utilizing quadtree partitioning, and applying image over-segmentation (superpixels) for precise contour extraction.

    • Main Results:

    • SmartLabel-2 demonstrates promising results in object labeling with a significantly small amount of labeled data (1%-5% of image size).
    • The method achieves close-to-fine extraction of object contours across diverse object categories.
    • Evaluations on six distinct object categories validate the effectiveness and efficiency of SmartLabel-2.

    Conclusions:

    • SmartLabel-2 offers an effective semi-automatic approach to image object labeling, substantially reducing manual effort.
    • The proposed method enhances the accuracy of object contour extraction and performs well even with limited labeled data.
    • SmartLabel-2 represents a significant advancement in creating training datasets for visual learning applications.