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Related Experiment Video

Updated: Jun 1, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

A tree-based context model for object recognition.

Myung Jin Choi¹, Antonio Torralba, Alan S Willsky

¹Two Sigma Investments, 379 West Broadway, New York, NY 10012, USA. myungjin@mit.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence

|June 15, 2011

Summary

This summary is machine-generated.

This study introduces a new dataset and an efficient context model for object recognition. The model improves scene interpretation and enables reliable image querying by leveraging contextual information among many object categories.

Related Experiment Videos

Last Updated: Jun 1, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Growing interest in using contextual information alongside local features for object detection and localization.
Previous context models limited by datasets with few object categories, hindering performance benefits.
Need for models that capture complex inter-category relationships in diverse scenes.

Purpose of the Study:

Introduce a novel dataset with numerous object categories and instances.
Propose an efficient context model for capturing inter-category contextual information.
Enhance object recognition and scene understanding capabilities.

Main Methods:

Development of a new dataset featuring images with many diverse object categories.
Proposal of an efficient tree-structured model to capture contextual information among over a hundred object categories.
Integration of global image features, object category dependencies, and local detector outputs within a probabilistic framework.

Main Results:

Demonstrated improvement in object recognition performance through the context model.
Achieved a coherent scene interpretation, enabling reliable multi-category image querying.
Showcased the model's ability to address scene understanding tasks beyond local detectors' capabilities.

Conclusions:

The proposed context model effectively utilizes contextual information for improved object recognition and scene understanding.
The model facilitates reliable image querying systems capable of handling multiple object categories.
The approach offers potential for advanced scene analysis, including detecting out-of-context objects and identifying typical/atypical scenes.