Lack of detailed land use (LU) information and efficient data collection methods have made the modeling of urban systems difficult. This study aims to develop a novel hierarchical rule-based LU extraction framework using geographic vector and remotely sensed (RS) data, in order to extract detailed subzonal LU information, residential LU in this study. The LU extraction system is developed to ex...

In this paper, we present a fast facial emotion classification system that relies on the concatenation of geometric and texture-based features. For classification, we propose to leverage the binary classification capabilities of a Support Vector Machine classifier to a hierarchical graph-based architecture that allows multi-class classification. We evaluate our classification results by calcula...

Information about current land-cover in forests is important for management and conservation of these areas. Up to the last decade traditional per pixel classification algorithms were used to be utilized in extracting land-cover information. However, they are poorly equipped to monitor land-cover in images acquired by current generation of satellite sensors with adequate accuracy. A good unders...

A. Additional Notation and Setup Let μ be the marginal distribution induced by D over X , and let p(x) be the distribution over [n] conditioned on X = x. For every function ` : [n]⇥ [k]!R+ and t 2 [k] let `t = [`(1, t), . . . , `(n, t)]> 2 R+. For every surrogate : [n]⇥ R!R+ let : R!R+ be a vector function such that y(u) = (y,u) for y 2 [n],u 2 Rd. For any integer d0 2 Z+ and pair of vectors u,...

We present a kernel-based algorithm for hierarchical text classification where the documents are allowed to belong to more than one category at a time. The classification model is a variant of the Maximum Margin Markov Network framework, where the classification hierarchy is represented as a Markov tree equipped with an exponential family defined on the edges. We present an efficient optimizati...

Hierarchical models have been shown to be effective in content classification. However, we observe through empirical study that the performance of a hierarchical model varies with given taxonomies; even a semantically sound taxonomy has potential to change its structure for better classification. By scrutinizing typical cases, we elucidate why a given semantics-based hierarchy does not work wel...

Hierarchical Multi-Label Classification is a complex classification task where the classes involved in the problem are hierarchically structured and each example may simultaneously belong to more than one class in each hierarchical level. In this paper, we extend our previous works, where we investigated a new local-based classification method that incrementally trains a multilayer perceptron f...

Phonemes in the English language can be represented using either parallel or hierarchical distinctive speech features. There have been a number of efforts to integrate multiple information sources but none of these efforts addressed the issue of combining multiple sets of articulatory/linguistic features with different organization topologies. In this study, we combine a frame-based parallel sp...

This paper looks into classification of documents that have hierarchical labels and are not restricted to a single label. Previous work in hierarchical classification focuses on the hierarchical perceptron (Hieron) algorithm. Hieron only supports single label learning. We investigate applying several standard multi-label learning techniques to Hieron. We then propose an extension of the algorit...

This paper presents an investigation into the summarisation of the free text element of questionnaire data using hierarchical text classification. The process makes the assumption that text summarisation can be achieved using a classification approach whereby several class labels can be associated with documents which then constitute the summarisation. A hierarchical classification approach is ...