@inbook{
 type = {inbook},
 year = {2012},
 keywords = {Kernel learning,Relevance analysis,Spectral clustering},
 pages = {501-510},
 websites = {http://link.springer.com/10.1007/978-3-642-34654-5_51},
 id = {11cc8733-739a-3654-8870-c72f46e17bfe},
 created = {2022-01-26T03:00:40.170Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {b9022d50-068c-31b4-9174-ebfaaf9ee57b},
 last_modified = {2022-01-26T03:00:40.170Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Molina-Giraldo2012},
 private_publication = {false},
 abstract = {In this paper an automatic image segmentation methodology based on Multiple Kernel Learning (MKL) is proposed. In this regard, we compute some image features for each input pixel, and then combine such features by means of a MKL framework. We automatically fix the weights of the MKL approach based on a relevance analysis over the original input feature space. Moreover, an unsupervised image segmentation measure is used as a tool to establish the employed kernel free parameter. A Kernel Kmeans algorithm is used as spectral clustering method to segment a given image. Experiments are carried out aiming to test the efficiency of the incorporation of weighted feature information into clustering procedure, and to compare the performance against state of the art algorithms, using a supervised image segmentation measure. Attained results show that our approach is able to compute a meaningful segmentations, demonstrating its capability to support further vision computer applications. © Springer-Verlag Berlin Heidelberg 2012.},
 bibtype = {inbook},
 author = {Molina-Giraldo, S. and Álvarez-Meza, A. M. and Peluffo-Ordoñez, D. H. and Castellanos-Domínguez, G.},
 doi = {10.1007/978-3-642-34654-5_51},
 chapter = {Image Segmentation Based on Multi-Kernel Learning and Feature Relevance Analysis},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

{"_id":"BzbthdchAb66zjAEN","bibbaseid":"molinagiraldo-lvarezmeza-peluffoordoez-castellanosdomnguez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2012","authorIDs":[],"author_short":["Molina-Giraldo, S.","Álvarez-Meza, A., M.","Peluffo-Ordoñez, D., H.","Castellanos-Domínguez, G."],"bibdata":{"type":"inbook","year":"2012","keywords":"Kernel learning,Relevance analysis,Spectral clustering","pages":"501-510","websites":"http://link.springer.com/10.1007/978-3-642-34654-5_51","id":"11cc8733-739a-3654-8870-c72f46e17bfe","created":"2022-01-26T03:00:40.170Z","file_attached":false,"profile_id":"aba9653c-d139-3f95-aad8-969c487ed2f3","group_id":"b9022d50-068c-31b4-9174-ebfaaf9ee57b","last_modified":"2022-01-26T03:00:40.170Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"Molina-Giraldo2012","private_publication":false,"abstract":"In this paper an automatic image segmentation methodology based on Multiple Kernel Learning (MKL) is proposed. In this regard, we compute some image features for each input pixel, and then combine such features by means of a MKL framework. We automatically fix the weights of the MKL approach based on a relevance analysis over the original input feature space. Moreover, an unsupervised image segmentation measure is used as a tool to establish the employed kernel free parameter. A Kernel Kmeans algorithm is used as spectral clustering method to segment a given image. Experiments are carried out aiming to test the efficiency of the incorporation of weighted feature information into clustering procedure, and to compare the performance against state of the art algorithms, using a supervised image segmentation measure. Attained results show that our approach is able to compute a meaningful segmentations, demonstrating its capability to support further vision computer applications. © Springer-Verlag Berlin Heidelberg 2012.","bibtype":"inbook","author":"Molina-Giraldo, S. and Álvarez-Meza, A. M. and Peluffo-Ordoñez, D. H. and Castellanos-Domínguez, G.","doi":"10.1007/978-3-642-34654-5_51","chapter":"Image Segmentation Based on Multi-Kernel Learning and Feature Relevance Analysis","title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","bibtex":"@inbook{\n type = {inbook},\n year = {2012},\n keywords = {Kernel learning,Relevance analysis,Spectral clustering},\n pages = {501-510},\n websites = {http://link.springer.com/10.1007/978-3-642-34654-5_51},\n id = {11cc8733-739a-3654-8870-c72f46e17bfe},\n created = {2022-01-26T03:00:40.170Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {b9022d50-068c-31b4-9174-ebfaaf9ee57b},\n last_modified = {2022-01-26T03:00:40.170Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Molina-Giraldo2012},\n private_publication = {false},\n abstract = {In this paper an automatic image segmentation methodology based on Multiple Kernel Learning (MKL) is proposed. In this regard, we compute some image features for each input pixel, and then combine such features by means of a MKL framework. We automatically fix the weights of the MKL approach based on a relevance analysis over the original input feature space. Moreover, an unsupervised image segmentation measure is used as a tool to establish the employed kernel free parameter. A Kernel Kmeans algorithm is used as spectral clustering method to segment a given image. Experiments are carried out aiming to test the efficiency of the incorporation of weighted feature information into clustering procedure, and to compare the performance against state of the art algorithms, using a supervised image segmentation measure. Attained results show that our approach is able to compute a meaningful segmentations, demonstrating its capability to support further vision computer applications. © Springer-Verlag Berlin Heidelberg 2012.},\n bibtype = {inbook},\n author = {Molina-Giraldo, S. and Álvarez-Meza, A. M. and Peluffo-Ordoñez, D. H. and Castellanos-Domínguez, G.},\n doi = {10.1007/978-3-642-34654-5_51},\n chapter = {Image Segmentation Based on Multi-Kernel Learning and Feature Relevance Analysis},\n title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}","author_short":["Molina-Giraldo, S.","Álvarez-Meza, A., M.","Peluffo-Ordoñez, D., H.","Castellanos-Domínguez, G."],"urls":{"Website":"http://link.springer.com/10.1007/978-3-642-34654-5_51"},"biburl":"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3","bibbaseid":"molinagiraldo-lvarezmeza-peluffoordoez-castellanosdomnguez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2012","role":"author","keyword":["Kernel learning","Relevance analysis","Spectral clustering"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inbook","creationDate":"2020-12-30T00:49:12.213Z","downloads":0,"keywords":["kernel learning","relevance analysis","spectral clustering"],"search_terms":["lecture","notes","computer","science","including","subseries","lecture","notes","artificial","intelligence","lecture","notes","bioinformatics","molina-giraldo","álvarez-meza","peluffo-ordoñez","castellanos-domínguez"],"title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","year":2012,"biburl":"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3","dataSources":["YEF3uFAbDNQXrkgNw","ya2CyA73rpZseyrZ8"]}