Indoor location recognition using fusion of SVM-based visual classifiers
- Resource Type
- Conference
- Authors
- Sjoberg, Mats; Koskela, Markus; Viitaniemi, Ville; Laaksonen, Jorma
- Source
- 2010 IEEE International Workshop on Machine Learning for Signal Processing Machine Learning for Signal Processing (MLSP), 2010 IEEE International Workshop on. :343-348 Aug, 2010
- Subject
- Signal Processing and Analysis
Communication, Networking and Broadcast Technologies
Computing and Processing
Fields, Waves and Electromagnetics
Detectors
Feature extraction
Training
Cameras
Visualization
Support vector machines
Robots
- Language
- ISSN
- 1551-2541
2378-928X
We apply our general-purpose algorithm for visual category recognition using bag-of-visual-words and other visual features and fusion of SVM classifiers to the recognition of indoor locations. This is an important application in many emerging fields, such as mobile augmented reality and autonomous robots. We evaluate the proposed method with other location recognition systems in the ImageCLEF 2010 RobotVision contest. The results show that given a large enough training set, a purely appearance-based method can perform very well - ranked first for one of the contest's training sets.