Purpose
Most Computer Vision and Machine Learning algorithms function by training on a large set of example inputs. To work effectively, most of these techniques require a large and varied set of training data. For example, the relatively well known real time face detection method used by Paul Viola and Micheal J. Jones was trained on 4916 hand labeled faces. However, acquiring a large volume of appropriate and usable images is often difficult. Furthermore, cropping and resizing many images, as well as marking point of interest by hand, is a tedious and time intensive task.
Historically, most datasets used in computer vision research have been tailored to the specific needs of the project being worked on. A large problem in comparing different computer vision techniques is the fact that most groups are using their own datasets. Each of these datasets may have different properties that make reported results from different methods harder to compare directly. For example, differences in image size, image quality, relative location of objects within the images, and level of occlusion and clutter present can lead to varying results
The Caltech 101 dataset aims to alleviate many of these common problems.
- The work of collecting a large set of images, and cropping and resizing them appropriately has been taken care of.
- A large number of different categories are represented, which benefits both single, and multi class recognition algorithms.
- Detailed object outlines have been marked for each image.
- By being released for general use, the Caltech 101 acts as a common standard by which to compare different algorithms without bias due to different datasets.
However, a recent study demonstrates that tests based on uncontrolled natural images (like the Caltech 101 dataset) can be seriously misleading, potentially guiding progress in the wrong direction.
Read more about this topic: Caltech 101
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