{"issue":"2","acknowledgement":"The authors acknowledge support from the EU projects CLASS (IST project 027978), PerAct (IST project 504321) and the EU Network of Excellence PASCAL2.","intvolume":"        88","extern":1,"_id":"3697","date_updated":"2021-01-12T07:49:02Z","publist_id":"2664","date_created":"2018-12-11T12:04:40Z","doi":"10.1007/s11263-009-0271-8","month":"06","author":[{"first_name":"Tinne","last_name":"Tuytelaars","full_name":"Tuytelaars,Tinne"},{"last_name":"Lampert","first_name":"Christoph","full_name":"Christoph Lampert","orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Blaschko,Matthew B","first_name":"Matthew","last_name":"Blaschko"},{"last_name":"Buntine","first_name":"Wray","full_name":"Buntine,Wray"}],"publication":"International Journal of Computer Vision","publication_status":"published","quality_controlled":0,"date_published":"2010-06-01T00:00:00Z","abstract":[{"text":"The goal of this paper is to evaluate and compare models and methods for learning to recognize basic entities in images in an unsupervised setting. In other words, we want to discover the objects present in the images by analyzing unlabeled data and searching for re-occurring patterns. We experiment with various baseline methods, methods based on latent variable models, as well as spectral clustering methods. The results are presented and compared both on subsets of Caltech256 and MSRC2, data sets that are larger and more challenging and that include more object classes than what has previously been reported in the literature. A rigorous framework for evaluating unsupervised object discovery methods is proposed.","lang":"eng"}],"publisher":"Springer","tmp":{"name":"Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)","image":"/images/cc_by_nc.png","legal_code_url":"https://creativecommons.org/licenses/by-nc/4.0/legalcode","short":"CC BY-NC (4.0)"},"status":"public","volume":88,"day":"01","citation":{"ieee":"T. Tuytelaars, C. Lampert, M. Blaschko, and W. Buntine, “Unsupervised object discovery: A comparison,” <i>International Journal of Computer Vision</i>, vol. 88, no. 2. Springer, pp. 284–302, 2010.","ama":"Tuytelaars T, Lampert C, Blaschko M, Buntine W. Unsupervised object discovery: A comparison. <i>International Journal of Computer Vision</i>. 2010;88(2):284-302. doi:<a href=\"https://doi.org/10.1007/s11263-009-0271-8\">10.1007/s11263-009-0271-8</a>","apa":"Tuytelaars, T., Lampert, C., Blaschko, M., &#38; Buntine, W. (2010). Unsupervised object discovery: A comparison. <i>International Journal of Computer Vision</i>. Springer. <a href=\"https://doi.org/10.1007/s11263-009-0271-8\">https://doi.org/10.1007/s11263-009-0271-8</a>","short":"T. Tuytelaars, C. Lampert, M. Blaschko, W. Buntine, International Journal of Computer Vision 88 (2010) 284–302.","ista":"Tuytelaars T, Lampert C, Blaschko M, Buntine W. 2010. Unsupervised object discovery: A comparison. International Journal of Computer Vision. 88(2), 284–302.","mla":"Tuytelaars, Tinne, et al. “Unsupervised Object Discovery: A Comparison.” <i>International Journal of Computer Vision</i>, vol. 88, no. 2, Springer, 2010, pp. 284–302, doi:<a href=\"https://doi.org/10.1007/s11263-009-0271-8\">10.1007/s11263-009-0271-8</a>.","chicago":"Tuytelaars, Tinne, Christoph Lampert, Matthew Blaschko, and Wray Buntine. “Unsupervised Object Discovery: A Comparison.” <i>International Journal of Computer Vision</i>. Springer, 2010. <a href=\"https://doi.org/10.1007/s11263-009-0271-8\">https://doi.org/10.1007/s11263-009-0271-8</a>."},"title":"Unsupervised object discovery: A comparison","page":"284 - 302","type":"journal_article","year":"2010"}