Add to ORKGThis paper presents an approach for improving the performance of kernel classifiers applied to object categorization problems. The approach is based on the use of distributions centered around each training points, which are exploited for inter-class invariant image representation with local invariant features. Furthermore, we propose an extensive use of unlabeled images for improving the SVMbased classifier. 1
Recently, methods based on local image features have shown promise for texture and object recognitio...
This paper considers the problem of multi-object categorization. We present an algorithm that combin...
This paper considers the problem of multi-object categorization. We present an algorithm that combin...
This paper presents an approach for improv-ing the performance of kernel classifiers ap-plied to obj...
This thesis extends the use of kernel learning techniques to specific problems of image classificati...
This thesis extends the use of kernel learning techniques to specific problems of image classificati...
In this paper, we propose a group-sensitive multiple kernel learning (GS-MKL) method to accommodate ...
International audienceRecently, methods based on local image features have shown promise for texture...
Recently, methods based on local image features have shown promise for texture and object recognitio...
Recently, methods based on local image features have shown promise for texture and object recognitio...
Recently, methods based on local image features have shown promise for texture and object recognitio...
Recently, methods based on local image features have shown promise for texture and object recognitio...
Recently, methods based on local image features have shown promise for texture and object recognitio...
Recently, methods based on local image features have shown promise for texture and object recognitio...
Recently, methods based on local image features have shown promise for texture and object recognitio...
Recently, methods based on local image features have shown promise for texture and object recognitio...
This paper considers the problem of multi-object categorization. We present an algorithm that combin...
This paper considers the problem of multi-object categorization. We present an algorithm that combin...
This paper presents an approach for improv-ing the performance of kernel classifiers ap-plied to obj...
This thesis extends the use of kernel learning techniques to specific problems of image classificati...
This thesis extends the use of kernel learning techniques to specific problems of image classificati...
In this paper, we propose a group-sensitive multiple kernel learning (GS-MKL) method to accommodate ...
International audienceRecently, methods based on local image features have shown promise for texture...
Recently, methods based on local image features have shown promise for texture and object recognitio...
Recently, methods based on local image features have shown promise for texture and object recognitio...
Recently, methods based on local image features have shown promise for texture and object recognitio...
Recently, methods based on local image features have shown promise for texture and object recognitio...
Recently, methods based on local image features have shown promise for texture and object recognitio...
Recently, methods based on local image features have shown promise for texture and object recognitio...
Recently, methods based on local image features have shown promise for texture and object recognitio...
Recently, methods based on local image features have shown promise for texture and object recognitio...
This paper considers the problem of multi-object categorization. We present an algorithm that combin...
This paper considers the problem of multi-object categorization. We present an algorithm that combin...