@conference {13070,
	title = {A novel feature descriptor based on the shearlet transform},
	booktitle = {Image Processing (ICIP), 2011 18th IEEE International Conference on},
	year = {2011},
	month = {2011/09//},
	pages = {1033 - 1036},
	abstract = {Problems such as image classification, object detection and recognition rely on low-level feature descriptors to represent visual information. Several feature extraction methods have been proposed, including the Histograms of Oriented Gradients (HOG), which captures edge information by analyzing the distribution of intensity gradients and their directions. In addition to directions, the analysis of edge at different scales provides valuable information. Shearlet transforms provide a general framework for analyzing and representing data with anisotropic information at multiple scales. As a consequence, signal singularities, such as edges, can be precisely detected and located in images. Based on the idea of employing histograms to estimate the distribution of edge orientations and on the accurate multi-scale analysis provided by shearlet transforms, we propose a feature descriptor called Histograms of Shearlet Coefficients (HSC). Experimental results comparing HOG with HSC show that HSC provides significantly better results for the problems of texture classification and face identification.},
	keywords = {analysis;multiscale, analysis;object, classification;face, classification;image, classification;intensity, coefficients;image, descriptor;feature, detection;object, distribution, edge, EXTRACTION, extraction;image, gradient, gradients;histograms, identification;feature, methods;histograms, of, orientations;face, oriented, recognition;feature, recognition;shearlet, recognition;transforms;, shearlet, singularities;texture, texture;object, transform;signal},
	doi = {10.1109/ICIP.2011.6115600},
	author = {Schwartz, W.R. and da Silva,R.D. and Davis, Larry S. and Pedrini,H.}
}
@article {13759,
	title = {Domain-Specific Term-List Expansion Using Existing Linguistic Resources},
	year = {2002},
	month = {2002/08//},
	institution = {Instititue for Advanced Computer Studies, Univ of Maryland, College Park},
	abstract = {This report describes a series of experiments involving expansion of a domain-specific human-generated "seed list" using available linguistic resources. The resources used for the expansion are intended to be general purpose: two large-scale Chinese-English dictionaries and a Chinese lexical knowledge base (HowNet). The methodology involves three steps: (1) hand extraction of head words from each entry in the human-generated seed list; (2) automatic comparison of these head words against entries in the linguistic resources-where an entry matches if the head word matches the entry exactly or is included in its the semantic definition; and (3) collection of any resulting matching entries into a larger term list. The terms extracted by this process were verified manually to confirm whether they were relevant to the topic of a specific domain. An important contribution of this work is the finding that the use of a bilingual term list for the expansion proces does not provide a significant improvement over the use of a simpler, more easily produced, monoligual term list.},
	keywords = {*LEXICOGRAPHY, *LINGUISTICS, AUTOMATIC, CHINA, COMPARISON, EXTRACTION, HANDS, knowledge based systems, linguistics, resources, WORDS(LANGUAGE)},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA525800},
	author = {Dorr, Bonnie J and Zhao,Tiejun}
}
