Columbia Object Image Library (COIL100)

Keywords

Artificial intelligence, Image (mathematics), Computer science, Object (grammar), Computer vision

References

• Early Visual LearningShree K. Nayar, Tomaso Poggio — 1996
• Real-time 100 object recognition systemShree K. Nayar, S.A. Nene, Hiroshi Murase — 1996

Cited by

• Navigation dans une base d'images à l'aide de graphes topologiquesMihaela Scuturici, Vasile-Marian Scuturici, Jérémy Clech — 2004
• An Evaluation of an Object Recognition Schema Using Multiple Region DetectorsMeritxell Vinyals, Arnau Ramisa, Ricardo Toledo — 2007
• Incremental Generalized Discriminative Common Vectors for Image ClassificationKathryn F. Whitmore, Caryl G. Crowell — 2015
• Multimedia Information Extraction in Ontology-based Semantic Annotation of Product CataloguesRoberto Bartolini, Emiliano Giovannetti, Simone Marchi — 2006
• Vector Space Embedding of Graphs via Statistics of Labelling InformationJaume Gibert Domingo — 2012
• A cognitive ego-vision system for interactive assistanceMarc Hanheide — 2006
• Mean Shift Segmentation - Evaluation of Optimization TechniquesJens N. Kaftan, André A. Bell, Til Aach — 2008
• Efficient Classification for Large-scale Problems by Multiple LDA SubspacesMartina Uray, Peter M. Roth, Horst Bischof — 2009
• The SOM reef - A new metaphoric visualization approach for self organizing mapsTim W. Nattkemper — 2005
• Semi-Supervised Classification by Low Density SeparationOlivier Chapelle, Alexander Zien, R. Cowell Z. Ghahramani — 2005
• Multiple Tasks are Better than One: Multi-task Learning and Feature Selection for Head Pose Estimation, Action Recognition and Event DetectionYan Yan — 2014
• Advanced non linear dimensionality reduction methods for multidimensional time series : applications to human motion analysisMichał Lewandowski — 2011
• Convex Sparse PCA for Unsupervised Feature LearningXiaojun Chang, Feiping Nie, Yi Yang — 2014
• From Markov Random Fields to Associative Memories and Back: Spin-Glass Markov Random FieldsBarbara Caputo, H. Niemann — 2007
• Why to Combine Reconstructive and Discriminative Information for Incremental Subspace LearningDaniel Skocaj, Martina Uray, Aleš Leonardis — 2006
• Visual region understanding : unsupervised extraction and abstractionGaurav Gupta — 2012
• Detecting domestic objects with ensembles of view-tuned support vector machine cascades trained on Web imagesMarco Kortkamp — 2011
• Supervised Probabilistic Robust Embedding with Sparse NoiseYu Zhang, Dit‐Yan Yeung, Eric P. Xing — 2012
• Automatic Feature Mining for Personalized Digital Image RetrievalKyoung-Mi Lee, W. Nick Street — 2001
• A Flexible Search-by-Similarity Algorithm for Content-Based Image RetrievalJérôme Fournier, Matthieu Cord — 2002

Related papers

• Nonlinear dimensionality reduction by locally linear embedding.Sam T. Roweis, Lawrence K. Saul — 2000
• Distinctive Image Features from Scale-Invariant KeypointsDavid Lowe — 2004
• A global geometric framework for nonlinear dimensionality reduction.Joshua B. Tenenbaum, Vin de Silva, John Langford — 2000
• Object recognition from local scale-invariant featuresDavid Lowe — 1999
• Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear ProjectionPeter N. Belhumeur, João P. Hespanha, David Kriegman — 1996
• A Combined Corner and Edge DetectorChris Harris, Matthew J. Stephens — 1988
• From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and PoseAthinodoros S. Georghiades, Peter N. Belhumeur, David Kriegman — 2001
• Normalized cuts and image segmentationJianbo Shi, Jitendra Malik — 2000
• Gradient-based learning applied to document recognitionYann LeCun, Léon Bottou, Yoshua Bengio — 1998
• Laplacian Eigenmaps for Dimensionality Reduction and Data RepresentationMikhail Belkin, Partha Niyogi — 2003