Food-101 - Mining discriminative components with random forests

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

Volumn 8694 LNCS, Issue PART 6, 2014, Pages 446-461

  Bossard, Lukas ^a   Guillaumin, Matthieu ^a   Van Gool, Luc ^a,b  

^a ETH ZURICH   (Switzerland)

^b UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

Discriminative part mining; Food recognition; Image classification; Random Forest

Indexed keywords

ALGORITHMS; IMAGE CLASSIFICATION;

CLASSIFICATION METHODS; COMPONENT BASED; DISCRIMINATIVE COMPONENTS; FISHER VECTORS; RANDOM FORESTS; RF COMPONENTS; SHARE KNOWLEDGE; SVM CLASSIFICATION;

DECISION TREES;

EID: 84906335989     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-10599-4_29     Document Type: Conference Paper

References (40)

1. Arandjelović, R., Zisserman, A.: Three things everyone should know to improve object retrieval. In: CVPR (2012)
2. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. In: ICCV (2006)
3. Bosch, A., Zisserman, A., Munoz, X.: Image Classification using Random Forests and Ferns. In: ICCV (2007)
4. Breiman, L.: Random forests. Machine Learning (2001)
5. Chen, M., Dhingra, K., Wu, W., Yang, L., Sukthankar, R., Yang, J.: PFID: Pittsburgh fast-food image dataset. In: ICIP (2009)
6. Chen, M.Y., Yang, Y.H., Ho, C.J., Wang, S.H., Liu, S.M., Chang, E., Yeh, C.H., Ouhyoung, M.: Automatic Chinese food identification and quantity estimation. In: SIGGRAPH Asia 2012 Technical Briefs (2012)
7. Doersch, C., Gupta, A., Efros, A.A.: Mid-level visual element discovery as discriminative mode seeking. In: NIPS (2013)
8. Endres, I., Shih, K., Jiaa, J., Hoiem, D.: Learning Collections of Part Models for Object Recognition. In: CVPR (2013)
9. Felzenszwalb, P.F., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. PAMI (2010)
10. Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient Graph-Based Image Segmentation. IJCV (2004)
11. Gall, J., Yao, A., Razavi, N., Van Gool, L., Lempitsky, V.: Hough forests for object detection, tracking, and action recognition. PAMI (2011)
12. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR (2014)
13. Hariharan, B., Malik, J., Ramanan, D.: Discriminative decorrelation for clustering and classification. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 459-472. Springer, Heidelberg (2012)
14. Ho, T.K.: Random decision forests. In: ICDAR (1995)
15. Hoashi, H., Joutou, T., Yanai, K.: Image Recognition of 85 Food Categories by Feature Fusion. In: ISM (2010)
16. Jia, Y.: Caffe: An open source convolutional architecture for fast feature embedding (2013), http://caffe.berkeleyvision.org/
17. Joachims, T., Finley, T., Yu, C.N.J.: Cutting-plane training of structural SVMs. Machine Learning (2009)
18. Joutou, T., Yanai, K.: A food image recognition system with Multiple Kernel Learning. In: ICIP (2009)
19. Juneja, M., Vedaldi, A., Jawahar, C., Zisserman, A.: Blocks That Shout: Distinctive Parts for Scene Classification. In: CVPR (2013)
20. Kawano, Y., Yanai, K.: Real-Time Mobile Food Recognition System. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (2013)
21. King, D.E.: Dlib-ml: A machine learning toolkit. JMLR (2009)
22. Kontschieder, P., Rota Bulò, S., Bischof, H., Pelillo, M.: Structured class-labels in random forests for semantic image labelling. In: ICCV (2011)
23. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)
24. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR (2006)
25. Li, Q., Wu, J., Tu, Z.: Harvesting mid-level visual concepts from large-scale internet images. In: CVPR (2013)
26. Malisiewicz, T., Gupta, A., Efros, A.A.: Ensemble of exemplar-svms for object detection and beyond. In: ICCV (2011)
27. Martin, C., Correa, J., Han, H., Allen, H., Rood, J., Champagne, C., Gunturk, B., Bray, G.: Validity of the remote food photography method (RFPM) for estimating energy and nutrient intake in near real-time. Obesity (2011)
28. Matsuda, Y., Hoashi, H., Yanai, K.: Multiple-Food Recognition Considering Cooccurrence Employing Manifold Ranking. In: ICPR (2012)
29. Moosmann, F., Nowak, E., Jurie, F.: Randomized clustering forests for image classification. PAMI (2008)
30. Noronha, J., Hysen, E., Zhang, H., Gajos, K.Z.: Platemate: crowdsourcing nutritional analysis from food photographs. In: ACM Symposium on UI Software and Technology (2011)
31. Quattoni, A., Torralba, A.: Recognizing indoor scenes. In: CVPR (2009)
32. Sánchez, J., Perronnin, F., Mensink, T., Verbeek, J.: Image Classification with the Fisher Vector: Theory and Practice. IJCV (2013)
33. Shotton, J., Johnson, M., Cipolla, R.: Semantic texton forests for image categorization and segmentation. In: CVPR (2008)
34. Singh, S., Gupta, A., Efros, A.A.: Unsupervised discovery of mid-level discriminative patches. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 73-86. Springer, Heidelberg (2012)
35. Sun, J., Ponce, J.: Learning discriminative part detectors for image classification and cosegmentation. In: ICCV (2013)
36. Uijlings, J.R.R., van de Sande, K.E.A., Gevers, T., Smeulders, A.W.M.: Selective search for object recognition. IJCV (2013)
37. Vedaldi, A., Fulkerson, B.: VLFeat: An open and portable library of computer vision algorithms (2008), http://www.vlfeat.org/
38. Wang, X., Wang, B., Bai, X., Liu, W., Tu, Z.: Max-margin multiple-instance dictionary learning. In: NIPS (2013)
39. Yang, S.L., Chen, M., Pomerleau, D., Sukthankar, R.: Food recognition using statistics of pairwise local features. In: CVPR (2010)
40. Yao, B., Khosla, A., Fei-Fei, L.: Combining randomization and discrimination for fine-grained image categorization. In: CVPR (2011)