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Caltech 101 is a data set of digital images created in September 2003 and compiled by Fei-Fei Li, Marco Andreetto, Marc 'Aurelio Ranzato and Pietro Perona at the California Institute of Technology. It is intended to facilitate Computer Vision research and techniques and is most applicable to techniques involving image recognition classification and categorization. Caltech 101 contains a total of 9,146 images, split between 101 distinct object categories (faces, watches, ants, pianos, etc.) and a background category. Provided with the images are a set of annotations describing the outlines of each image, along with a Matlab script for viewing. ==Purpose== Most Computer Vision and Machine Learning algorithms function by training on example inputs. They require a large and varied set of training data to work effectively. For example, the real-time face detection method used by Paul Viola and Michael J. Jones was trained on 4,916 hand-labeled faces.〔P. Viola and M. J. Jones, Robust Real-Time Object Detection, , IJCV 2004〕 Cropping, re-sizing and hand-marking points of interest is tedious and time-consuming. Historically, most data sets used in computer vision research have been tailored to the specific needs of the project being worked on.A large problem in comparing computer vision techniques is the fact that most groups use their own data sets. Each set 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.〔Oertel, C., Colder, B., Colombe, J., High, J., Ingram, M., Sallee, P., Current Challenges in Automating Visual Perception. Proceedings of IEEE Advanced Imagery Pattern Recognition Workshop 2008〕 The Caltech 101 data set aims at alleviating many of these common problems. *The images are cropped and re-sized. *Many categories are represented, which suits both single and multiple class recognition algorithms. *Detailed object outlines are marked. *Available for general use, Caltech 101 acts as a common standard by which to compare different algorithms without bias due to different data sets. However, a recent study 〔( Why is Real-World Visual Object Recognition Hard? Pinto N, Cox DD, DiCarlo JJ PLoS Computational Biology Vol. 4, No. 1, e27 ) 〕 demonstrates that tests based on uncontrolled natural images (like the Caltech 101 data set) can be seriously misleading, potentially guiding progress in the wrong direction. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Caltech 101」の詳細全文を読む スポンサード リンク
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