Making Visual Object Categorization More Challenging: Randomized Caltech-101 Data Set

Visual object categorization is one of the most active research topics in computer vision, and Caltech-101 data set is one of the standard benchmarks for evaluating the method performance. Despite of its wide use, the data set has certain weaknesses: (i) the objects are practically in a standard pose and scale in the middle of the images and (ii) background varies too little in certain categories making it more discriminative than the foreground objects. In this work, we demonstrate how these weaknesses bias the evaluation results in an undesired manner. In addition, we reduce the bias effect by replacing the backgrounds with random landscape images from Google and by applying random Euclidean transformations to the foreground objects. We demonstrate how the proposed randomization process makes visual object categorization more challenging improving the relative results of methods which categorize objects by their visual appearance and are invariant to pose changes. The new data set is made publicly available for other researchers.