One-shot learning of object categories using dependent Gaussian processes

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6376 LNCS, Issue , 2010, Pages 232-241

  Rodner, Erik ^a   Denzler, Joachim ^a  

^a FRIEDRICH SCHILLER UNIVERSITY JENA   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ART IMAGE; GAUSSIAN PROCESSES; IMAGE CATEGORIZATION; INDEPENDENT LEARNING; KERNEL FUNCTION; KNOWLEDGE TRANSFER; LEXICAL DATABASE; MODEL SELECTION TECHNIQUES; NON-PARAMETRIC BAYESIAN; OBJECT CATEGORIES; ONE-SHOT LEARNING; SELECTION PROCESS; SEMANTIC SIMILARITY; TRAINING EXAMPLE; WORDNET;

GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); KNOWLEDGE MANAGEMENT;

PATTERN RECOGNITION;

EID: 78349246816     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15986-2_24     Document Type: Conference Paper

References (21)

1. Pedersen, T., Patwardhan, S., Michelizzi, J.: Wordnet: Similarity - measuring the relatedness of concepts. In: Fifth Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL 2004), pp. 38-41 (2004)
2. Ahn, W., Brewer, W.F., Mooney, R.J.: Schema acquisition from a single example. J. of Experim. Psychology: Learning, Memory, and Cognition 18, 391-412 (1992)
3. Fei-Fei, L., Fergus, R., Perona, P.: One-shot learning of object categories. PAMI 28(4), 594-611 (2006)
4. Tommasi, T., Caputo, B.: The more you know, the less you learn: from knowledge transfer to one-shot learning of object categories. In: BMVC (2009)
5. Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. Adaptive Computation and Machine Learning. The MIT Press, Cambridge (2005)
6. Kapoor, A., Grauman, K., Urtasun, R., Darrell, T.: Gaussian processes for object categorization. IJCV 88(2), 169-188 (2009)
7. Lawrence, N.D., Platt, J.C., Jordan, M.I.: Extensions of the informative vector machine. In: Deterministic and Statist. Methods in Machine Learn., pp. 56-87 (2004)
8. Urtasun, R., Quattoni, A., Lawrence, N.D., Darrell, T.: Transfering nonlinear representations using gaussian processes with a shared latent space. In: Proceedings of the Learning Workshop (Snowbird) (2008), MIT-CSAIL-TR-2008- 020
9. Chai, K.M.: Generalization errors and learning curves for regression with multi-task gaussian processes. In: NIPS, pp. 279-287 (2009)
10. Bonilla, E., Chai, K.M., Williams, C.: Multi-task gaussian process prediction. In: NIPS, pp. 153-160. MIT Press, Cambridge (2008)
11. Cao, B., Pan, S.J., Zhang, Y., Yeung, D.Y., Yang, Q.: Adaptive transfer learning. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (2010)
12. Nickisch, H., Rasmussen, C.E.: Approximations for binary gaussian process classification. Journal of Machine Learning Research 9, 2035-2078 (2008)
13. Pillonetto, G., Dinuzzo, F., Nicolao, G.D.: Bayesian online multitask learning of gaussian processes. PAMI 2, 193-205 (2010)
14. Torralba, A., Murphy, K.P., Freeman, W.T.: Sharing visual features for multiclass and multiview object detection. PAMI 29(5), 854-869 (2007)
15. van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating color descriptors for object and scene recognition. PAMI (2010) (in press)
16. Kiefer, J.: Sequential minimax search for a maximum. Proceedings of the American Mathematical Society 4(3), 502-506 (1953)
17. Rohrbach, M., Stark, M., Szarvas, G., Schiele, B., Gurevych, I.: What helps where - and why? semantic relatedness for knowledge transfer. In: CVPR (2010)
18. Marszalek, M., Schmid, C.: Semantic hierarchies for visual object recognition. In: CVPR, pp. 1-7 (2007)
19. Moosmann, F., Triggs, B., Jurie, F.: Fast discriminative visual codebooks using randomized clustering forests. In: NIPS, pp. 985-992 (2006)
20. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR, pp. 2169-2178 (2006)
21. Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: CIVR: Conference on Image and Video Retrieval, pp. 401-408 (2007)