Machine learning for adaptive image interpretation

Authors:
Ilya Levner;Vadim Bulitko
Affiliations:
Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada;Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
Venue:
IAAI'04 Proceedings of the 16th conference on Innovative applications of artifical intelligence
Year:
2004

Citing 4
Cited 6

Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
TIDA: an algorithm for the delineation of tree crowns in high spatial resolution remotely sensed imagery

Computers & Geosciences - Intelligent methods for processing geodata
Adapting Object Recognition across Domains: A Demonstration

ICVS '01 Proceedings of the Second International Workshop on Computer Vision Systems
A Trainable Hierarchical Hidden Markov Tree Model for Color Image Annotation

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1

GAMM: genetic algorithms with meta-models for vision

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Genetic algorithms for action set selection across domains: a demonstration

Proceedings of the 8th annual conference on Genetic and evolutionary computation
Design of experiments for performance evaluation and parameter tuning of a road image processing chain

EURASIP Journal on Applied Signal Processing
Incremental system engineering using process networks

PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Modeling, evaluation and control of a road image processing chain

SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
3D Scene interpretation by combining probability theory and logic: The tower of knowledge

Computer Vision and Image Understanding

Quantified Score

Hi-index	0.00

Visualization

Abstract

Automated image interpretation is an important task with numerous applications. Until recently, designing such systems required extensive subject matter and computer vision expertise resulting in poor cross-domain portability and expensive maintenance. Recently, a machine-learned system (ADORE) was successfully applied in an aerial image interpretation domain. Subsequently, it was re-trained for another man-made object recognition task. In this paper we propose and implement several extensions of ADORE addressing its primary limitations. These extensions enable the first successful application of this emerging AI technology to a natural image interpretation domain. The resulting system is shown to be robust with respect to noise in the training data, illumination, and camera angle variations as well as competitively adaptive with respect to novel images.