@InProceedings{WangBoye:2013:FeLeMu,
               author = "Wang, Quan and Boyer, Kim L.",
          affiliation = "{Rensselaer Polytechnic Institute} and {Rensselaer Polytechnic 
                         Institute}",
                title = "Feature Learning by Multidimensional Scaling and its Applications 
                         in Object Recognition",
            booktitle = "Proceedings...",
                 year = "2013",
               editor = "Boyer, Kim and Hirata, Nina and Nedel, Luciana and Silva, 
                         Claudio",
         organization = "Conference on Graphics, Patterns and Images, 26. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Feature learning, image distance measurement, multidimensional 
                         scaling, spatial pyramid matching.",
             abstract = "We present the MDS feature learning framework, in which 
                         multidimensional scaling (MDS) is applied on high-level pairwise 
                         image distances to learn fixed-length vector representations of 
                         images. The aspects of the images that are captured by the learned 
                         features, which we call MDS features, completely depend on what 
                         kind of image distance measurement is employed. With properly 
                         selected semantics-sensitive image distances, the MDS features 
                         provide rich semantic information about the images that is not 
                         captured by other feature extraction techniques. In our work, we 
                         introduce the iterated Levenberg-Marquardt algorithm for solving 
                         MDS, and study the MDS feature learning with IMage Euclidean 
                         Distance (IMED) and Spatial Pyramid Matching (SPM) distance. We 
                         present experiments on both synthetic data and real images the 
                         publicly accessible UIUC car image dataset. The MDS features based 
                         on SPM distance achieve exceptional performance for the car 
                         recognition task.",
  conference-location = "Arequipa, Peru",
      conference-year = "5-8 Aug. 2013",
                  doi = "10.1109/SIBGRAPI.2013.11",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2013.11",
             language = "en",
                  ibi = "8JMKD3MGPBW34M/3ED4F9S",
                  url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3ED4F9S",
           targetfile = "MDS_SIBGRAPI_2013.pdf",
        urlaccessdate = "2024, Apr. 27"
}