We propose a new distance estimation technique by boosting and apply it to enhance the effectiveness of classifier when
the training set is insufficient. The proposed method is called Boosted Distance based on local and global dissimilarity
representation (BDLGDR). It is a modified method of Boosted Distance. Rather than simply differentiating the feature
vectors, we calculate a new dissimilarity representation of each couple of feature vectors. This new dissimilarity
representation contains two parts: local dissimilarity representation part and global dissimilarity representation part. The
proposed method does not only achieve high classification accuracy when the training set is insufficient but when the
number of training set is sufficient it also can achieve as high accuracy as AdaBoost. The method has been thoroughly
tested on several databases of high-resolution (1.25m) Terra-SAR images. In the first experiment, we decreased the
number of the training sample per class from 10 to 1. The result showed that the proposed method outperformed both
Boosted Distance and AdaBoost. In the second experiment, we used sufficient training samples. The experimental result
illuminated that the proposed method performed at least as well as AdaBoost and needed fewer iteration rounds to
converge than Boosted Distance.
Particle filter has attracted much attention due to its robust tracking performance in clutter. However, a price to pay for its robustness is the computational cost. Meanwhile there is no exact mechanism for choosing or updating scale in its framework for accurate tracking. In this paper we propose a threshold and scale based particle filter (TSPF). It employs a threshold to discard the bad particles and keep the good ones. In this case, the efficiency of particles is improved and the number of required particles is greatly reduced. It also adapts Robert T. Collins's theory of selecting kernel scale for mean shift blob tracking to particle filter. Experiments show TSPF works well, both spatially and in scale.