@inproceedings{Siagian_Itti05wapcv,
  author = {C. Siagian and L. Itti},
  title = {Gist: A Mobile Robotics Application of Context-Based Vision in Outdoor Environment},
  abstract = {We present context-based scene recognition for mobile robotics applications. Our classifier is able to differentiate outdoor scenes without temporal and spatial filtering relatively well from a variety of locations at a college campus using a set of features that together capture the ``gist'' of the scene. We discuss and perform experiments on the accuracy and scalability of the current features. We compare the classification accuracy of a set of scenes from 1551 frames filmed outdoors along a path and dividing them to four and twelve different legs. We obtained a classification rate of 67.96 percent and 48.61 percent, respectively. We also tested the scalability of the features by comparing the classification results from the previous scenes with four legs with a longer path with eleven legs. We obtained a classification rate of 55.08 percent. In the end we also put forth some ideas to improve upon the theoretical strength of the gist features.},
  booktitle = {Proc. IEEE-CVPR Workshop on Attention and Performance in Computer Vision (WAPCV'05), San Diego, California},
  pages = {1-7},
  year = {2005},
  month = {Jun},
  type = {bu;mod;cv},
  file = {http://iLab.usc.edu/publications/doc/Siagian_Itti05wapcv.pdf},
  review = {full/wkshp}
}