@conference {13068,
	title = {Face verification using large feature sets and one shot similarity},
	booktitle = {Biometrics (IJCB), 2011 International Joint Conference on},
	year = {2011},
	month = {2011/10//},
	pages = {1 - 8},
	abstract = {We present a method for face verification that combines Partial Least Squares (PLS) and the One-Shot similarity model[28]. First, a large feature set combining shape, texture and color information is used to describe a face. Then PLS is applied to reduce the dimensionality of the feature set with multi-channel feature weighting. This provides a discriminative facial descriptor. PLS regression is used to compute the similarity score of an image pair by One-Shot learning. Given two feature vector representing face images, the One-Shot algorithm learns discriminative models exclusively for the vectors being compared. A small set of unlabeled images, not containing images belonging to the people being compared, is used as a reference (negative) set. The approach is evaluated on the Labeled Face in the Wild (LFW) benchmark and shows very comparable results to the state-of-the-art methods (achieving 86.12\% classification accuracy) while maintaining simplicity and good generalization ability.},
	keywords = {analysis;set, approximations;regression, descriptor;labeled, Face, feature, in, information;face, information;texture, least, LFW;PLS;PLS, recognition;least, regression;color, sets;one, shot, similarity;partial, squares, squares;shape, the, theory;, verification;facial, wild;large},
	doi = {10.1109/IJCB.2011.6117498},
	author = {Guo,Huimin and Robson Schwartz,W. and Davis, Larry S.}
}
@conference {13073,
	title = {Image ranking and retrieval based on multi-attribute queries},
	booktitle = {Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on},
	year = {2011},
	month = {2011/06//},
	pages = {801 - 808},
	abstract = {We propose a novel approach for ranking and retrieval of images based on multi-attribute queries. Existing image retrieval methods train separate classifiers for each word and heuristically combine their outputs for retrieving multiword queries. Moreover, these approaches also ignore the interdependencies among the query terms. In contrast, we propose a principled approach for multi-attribute retrieval which explicitly models the correlations that are present between the attributes. Given a multi-attribute query, we also utilize other attributes in the vocabulary which are not present in the query, for ranking/retrieval. Furthermore, we integrate ranking and retrieval within the same formulation, by posing them as structured prediction problems. Extensive experimental evaluation on the Labeled Faces in the Wild(LFW), FaceTracer and PASCAL VOC datasets show that our approach significantly outperforms several state-of-the-art ranking and retrieval methods.},
	keywords = {datasets;image, datasets;PASCAL, faces, FaceTracer, in, methods;labeled, queries;image, ranking;image, retrieval, retrieval;, the, VOC, wild;multiattribute},
	doi = {10.1109/CVPR.2011.5995329},
	author = {Siddiquie,B. and Feris,R.S. and Davis, Larry S.}
}
@conference {12106,
	title = {A Framework for Software Engineering Experimental Replications},
	booktitle = {Engineering of Complex Computer Systems, 2008. ICECCS 2008. 13th IEEE International Conference on},
	year = {2008},
	month = {2008/04/31/3},
	pages = {203 - 212},
	abstract = {Experimental replications are very important to the advancement of empirical software engineering. Replications are one of the key mechanisms to confirm previous experimental findings. They are also used to transfer experimental knowledge, to train people, and to expand a base of experimental evidence. Unfortunately, experimental replications are difficult endeavors. It is not easy to transfer experimental know-how and experimental findings. Based on our experience, this paper discusses this problem and proposes a Framework for Improving the Replication of Experiments (FIRE). The FIRE addresses knowledge sharing issues both at the intra-group (internal replications) and inter-group (external replications) levels. It encourages coordination of replications in order to facilitate knowledge transfer for lower cost, higher quality replications and more generalizable results.},
	keywords = {engineering, engineering;, evidence;experimental, experimental, Experiments;experimental, for, Framework, Improving, knowledge, of, replication, replication;software, sharing;software, the, transfer;knowledge},
	doi = {10.1109/ICECCS.2008.38},
	author = {Mendonca,M.G. and Maldonado,J.C. and de Oliveira,M.C.F. and Carver, J. and Fabbri,C.P.F. and Shull, F. and Travassos,G.H. and Hohn,E.N. and Basili, Victor R.}
}
