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@InProceedings{KlossCirSilPedSch:2015:PaLeSq,
               author = "Kloss, Ricardo Barbosa and Cirne, Marcos Vinicius Mussel and 
                         Silva, Samira and Pedrini, H{\'e}lio and Schwartz, William 
                         Robson",
          affiliation = "{Universidade Federal de Minas Gerais} and {Universidade de 
                         Campinas} and {Universidade Federal de Minas Gerais} and 
                         {Universidade de Campinas} and {Universidade Federal de Minas 
                         Gerais}",
                title = "Partial Least Squares Image Clustering",
            booktitle = "Proceedings...",
                 year = "2015",
               editor = "Papa, Jo{\~a}o Paulo and Sander, Pedro Vieira and Marroquim, 
                         Ricardo Guerra and Farrell, Ryan",
         organization = "Conference on Graphics, Patterns and Images, 28. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Image Clustering, Partial Least Squares, Video Summarization, Shot 
                         Sampling.",
             abstract = "Clustering techniques have been widely used in areas that handle 
                         massive amounts of data, such as statistics, information 
                         retrieval, data mining and image analysis. This work presents a 
                         novel image clustering method called Partial Least Square Image 
                         Clustering (PLSIC), which employs a one-against-all Partial Least 
                         Squares classifier to find image clusters with low redundancy 
                         (each cluster represents different visual concept) and high purity 
                         (two visual concepts should not be in the same cluster). The main 
                         goal of the proposed approach is to find groups of images in an 
                         arbitrary set of unlabeled images to convey well defined visual 
                         concepts. As a case study, we evaluate the PLSIC to the video 
                         summarization problem by means of experiments with 50 videos from 
                         various genres of the Open Video Project, comparing summaries 
                         generated by the PLSIC with other video summarization approaches 
                         found in the literature. A experimental evaluation demonstrates 
                         that the proposed method can produce very satisfactory results.",
  conference-location = "Salvador",
      conference-year = "Aug. 26-29, 2015",
             language = "en",
           targetfile = "PID3763835.pdf",
        urlaccessdate = "2021, Dec. 07"
}


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