FOREST - A Flexible Object Recognition System

Julia Moehrmann, Gunther Heidemann

Abstract

Despite the growing importance of image data, image recognition has succeeded in taking a permanent role in everyday life in specific areas only. The reason is the complexity of currently available software and the difficulty in developing image recognition systems. Currently available software frameworks expect users to have a comparatively high level of programming and computer vision skills. FOREST – a flexible object recognition framework – strives to overcome this drawback. It was developed for non-expert users with little-to-no knowledge in computer vision and programming. While other image recognition systems focus solely on the recognition functionality, FOREST covers all steps of the development process, including selection of training data, ground truth annotation, investigation of classification results and of possible skews in the training data. The software is highly flexible and performs the computer vision functionality autonomously by applying several feature detection and extraction operators in order to capture important image properties. Despite the use of weakly supervised learning, applications developed with FOREST achieve recognition rates between 86 and 99% and are comparable to state-of-the-art recognition systems.

References

  1. Chiu, K. (2011). Vision On Tap : An Online Computer Vision Toolkit. Master's thesis, Massachusetts Institute of Technology. Dept. of Architecture. Program in Media Arts and Sciences.
  2. Fails, J. and Olsen, D. (2003). A Design Tool for Camerabased Interaction. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 449-456. ACM.
  3. Fei-Fei, L., Fergus, R., and Perona, P. (2004). Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories. In IEEE CVPR Workshop on Generative-Model based Vision.
  4. Hegazy, D. and Denzler, J. (2009). Generic Object Recognition. In Computer Vision in Camera Networks for Analyzing Complex Dynamic Natural Scenes.
  5. Klemmer, S., Li, J., Lin, J., and Landay, J. (2004). PapierMaˆché: Toolkit Support for Tangible Input. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 399-406. ACM.
  6. Lowe, D. (2004). Distinctive Image Features from ScaleInvariant Keypoints. Intl. Journal of Computer Vision, 60:91-110.
  7. Manjunath, B., Ohm, J.-R., Vasudevan, V., and Yamada, A. (2001). Color and Texture Descriptors. IEEE Transactions on Circuits and Systems for Video Technology, 11(6):703-715.
  8. Matas, J., Chum, O., Urban, M., and Pajdla, T. (2002). Robust Wide Baseline Stereo from Maximally Stable Extremal Regions. In British Machine Vision Conference, volume 1, pages 384-393.
  9. Mikolajczyk, K. and Schmid, C. (2004). Scale and Affine Invariant Interest Point Detectors. Intl. Journal of Computer Vision, 60(1):63-86.
  10. Moehrmann, J. and Heidemann, G. (2013). SemiAutomatic Image Annotation. In Computer Analysis of Images and Patterns, volume 8048 of Lecture Notes in Computer Science, pages 266-273.
  11. Nilsback, M.-E. and Zisserman, A. (2006). A Visual Vocabulary for Flower Classification. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), volume 2, pages 1447-1454. IEEE.
  12. Opelt, A., Pinz, A., Fussenegger, M., and Auer, P. (2006). Generic Object Recognition with Boosting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(3):416-431.
  13. Samaria, F. and Harter, A. (1994). Parameterisation of a Stochastic Model for Human Face Identification. In Proceedings of the IEEE Workshop on Applications of Computer Vision, pages 138-142. IEEE.
  14. Varma, M. and Ray, D. (2007). Learning The Discriminative Power-Invariance Trade-Off. IEEE Intl. Conference on Computer Vision (ICPR), 0:1-8.
  15. Zhang, W., Yu, B., Zelinsky, G., and Samaras, D. (2005). Object Class Recognition using Multiple Layer Boosting with Heterogeneous Features. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), volume 2, pages 323-330.
Download


Paper Citation


in Harvard Style

Moehrmann J. and Heidemann G. (2015). FOREST - A Flexible Object Recognition System . In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM, ISBN 978-989-758-077-2, pages 119-127. DOI: 10.5220/0005175901190127


in Bibtex Style

@conference{icpram15,
author={Julia Moehrmann and Gunther Heidemann},
title={FOREST - A Flexible Object Recognition System},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,},
year={2015},
pages={119-127},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005175901190127},
isbn={978-989-758-077-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,
TI - FOREST - A Flexible Object Recognition System
SN - 978-989-758-077-2
AU - Moehrmann J.
AU - Heidemann G.
PY - 2015
SP - 119
EP - 127
DO - 10.5220/0005175901190127