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 we use the SNoW learning architecture to learn representations for the 100 objects in the Columbia Object Image Database (COIL-100). Experimental results show that the SNoW-based method outperforms 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.
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