The Problem: While a tracking system is unaware of the identity of any object it tracks, the identity remains the same for the entire tracking sequence. Our system leverages this information by using accumulated joint cooccurrences of the representations within the sequence to create a hierarchical binary-tree classifier of the representations. This classifier is useful to classify sequences as...

Recently, logical analysis of data (LAD) using a classifier based on a linear combination of patterns has been introduced, providing high classification accuracy and pattern-based interpretability on classification results. However, it is known that most of LAD-based multi-classification algorithms have conflicts between classification accuracy and computational complexity because they are base...

We investigate the memory structure and retrieval of the brain and propose a hybrid neural network of addressable and content-addressable memory which is a special database model and can memorize and retrieve any piece of information (a binary pattern) both addressably and content-addressably. The architecture of this hybrid neural network is hierarchical and takes the form of a tree of slabs w...

Compressing an image is significantly different than compressing raw binary data. For this different compression algorithm are used to compress images. Discrete wavelet transform has been widely used to compress the image. Wavelet transform are very powerful compared to other transform because its ability to describe any type of signals both in time and frequency domain simultaneously. The prop...

We consider the use of reranking as a way to relax typical independence assumptions often made in hierarchical multilabel classification. Our reranker is based on (i) an algorithm that generates promising k-best classification hypotheses from the output of local binary classifiers that classify nodes of a target tree-shaped hierarchy; and (ii) a tree kernel-based reranker applied to the classif...

We describe the use of a binary hierarchical clustering (BHC) framework for clustering of gene expression data. The BHC algorithm involves two major steps. Firstly, the K-means algorithm is used to split the data into two classes. Secondly, the Fisher criterion is applied to the classes to assess whether the splitting is acceptable. The algorithm is applied to the sub-classes recursively and en...

Hierarchical clustering is a popular method for finding structure in multivariate data, resulting in a binary tree constructed on the particular objects of the study, usually sampling units. The user faces the decision where to cut the binary tree in order to determine the number of clusters to interpret and there are various ad hoc rules for arriving at a decision. A simple permutation test is...

In this paper we investigate Object Oriented Binary Space Partitioning. The Binary Space Partition Tree (BSP-Trees) is a widely used and e ective data structure for solid modeling and hidden surface removal. We present algorithms for eÆciently constructing Object BSP Trees(OBSP) in 2-Dimensions. The term object used arises since the construction of our tree utilizes the property of hierarchical...

We propose a new splitting criterion for a meta-learning approach to multiclass classifier design that adaptively merges the classes into a tree-structured hierarchy of increasingly difficult binary classification problems. The classification tree is constructed from empirical estimates of the Henze-Penrose bounds on the pairwise Bayes misclassification rates that rank the binary subproblems in...

We describe the use of a binary hierarchical clustering (BHC) framework for clustering of gene expression data. The BHC algorithm involves two major steps. Firstly, the K-means algorithm is used to split the data into two classes. Secondly, the Fisher criterion is applied to the classes to assess whether the splitting is acceptable. The algorithm is applied to the sub-classes recursively and en...