A learning account for the problem of object recognition is developed within the PAC (Probably Approximately Correct) model of learnability. The proposed approach makes no assumptions on the distribution of the observed objects, but quantifies success relative to its past experience. Most importantly, the success of learning an object representation is naturally tied to the ability to represent it as a function of some intermediate representations extracted from the image. We evaluate this approach in a large scale experimental study in which the SNoW learning architecture is used to learn representations for the 100 objects in the Columbia Object Image Database (COIL-100). The SNoW-based method is shown to outperform other methods in terms of recognition rates; its performance degrades gracefully when the training data contains fewer views and in the presence of occlusion noise.
|Number of pages||8|
|Journal||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Publication status||Published - 2000|
|Event||CVPR '2000: IEEE Conference on Computer Vision and Pattern Recognition - Hilton Head Island, SC, USA|
Duration: 2000 Jun 13 → 2000 Jun 15
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition