Evaluating classifier combination in object classification
Authors: 
Jian Hou, Xu E, Qi Xia, Nai-Ming QiAuthors Info & Claims
Pages 799 - 816
Abstract
Classifier combination is used in object classification to combine the strength of multiple complementary classifiers and yield better performance than any single classifier. While various optimization-based combination methods have been presented in literature, their real effectiveness in practice has been called in question. This prompts us to investigate the behavior of classifiers in combination with the simple average combination method. Specifically, we investigate the influence of some issues on average classifier combination performance with extensive experiments on four diverse datasets. As a result, we find that the behavior of features and kernel functions in feature combination, and of soft labels and classifiers in classifier fusion, can be elegantly explained in the framework of the kNN method in instance-based learning. This framework shows that by proper selection of features, kernel functions, soft labels and classifiers, an enhanced average combination is able to perform much better than the average combination of all features, kernel functions, soft labels and classifiers. Furthermore, this framework gives rise to the descending combination performance curve (DCPC) as a new performance evaluation criterion of combination methods. Unlike the ordinary criterion of comparing only the final classification rate, DCPC also captures the ability of combination methods to combine the strength and avoid the drawbacks of multiple classifiers.
References
[1]
Altmann A, Rosen-Zve M, Prosperi M, Aharoni E, Neuvirth H et al (2008) Comparison of classifier fusion methods for predicting response to anti hiv-1 therapy. PLoS One 3(10):1---9
[2]
Barla A, Odone F, Verri A (2003) Histogram intersection kernel for image classification. In: International conference on image processing, pp 513---516
[3]
Bay H, Ess A, Tuytelaars T, Gool LV (2008) Surf: speeded up robust features. Comput Vis Image Underst 110(3):346---359
[4]
Bickel S, Scheffer T (2004) Multi-view clustering. In: International conference on data mining, pp 19---26
[5]
Bosch A, Zisserman A, Munoz X (2007) Representing shape with a spatial pyramid kernel. In: ACM international conference on image and video retrieval, pp 401---408
[6]
Chibelushi CC, Deravi F, Mason JSD (1999) Adaptive classifier integration for robust pattern recognition. IEEE Trans Syst Man Cybern Part B Cybern 29(6):902---907
[7]
Dalal N, Triggs B (2005) Histogram of oriented graidents for human detection. IEEE Int Conf Comput Vis Pattern Recogn 1:886---893
[8]
Duin RPW, Juszczak P, Paclik P, Pekalska E, Ridder D, Tax DMJ, Verzakov S (2007) Prtools4.1, a matlab toolbox for pattern recognition. In: Technical report, Delft University of Technology
[9]
Fei-Fei L, Fergus R, Perona P (2004) Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: CVPR workshop on generative-model based vision, p 178
[10]
Gehler P, Nowozin S (2009) On feature combination for multiclass object classification. In: IEEE international conference on computer vision, pp 221---228
[11]
Gonen M, Alpaydin E (2008) Localized multiple kernel learning. In: International conference on machine learning, pp 642---651
[12]
Heerden CV, Barnard E (2009) Combining multiple classifiers for age classification. In: 20th annual symposium of the Pattern Recognition Association of South Africa, pp 59---64
[13]
Ho TK, Hull JJ, Srihari SN (1994) Decision combination in multiple classifier systems. IEEE Trans Pattern Anal Mach Intell 16(1):66---75
[14]
Hou J, Pelillo M (2013) A simple feature combination method based on dominant sets. Pattern Recogn Lett 46(11):3129---3139
[15]
Hou J, Zhang BP, Qi NM, Yang Y (2011) Evaluating feature combination in object classification. In: International symposium on visual computing, pp 597---606
[16]
Jia LL, Fei-Fei L (2007) What, where and who? Classifying event by scene and object recognition. In: IEEE international conference on computer vision, pp 1---8
[17]
Kim HC, Ghahramani Z (2012) Bayesian classifier combination. In: International conference on artificial intelligence and statistics, pp 619---627
[18]
Kloft M, Brefeld U, Sonnenburg S, Zien A (2011) Lp-norm multiple kernel learning. J Mach Learn Res 12:953---977
[19]
Kumar A, Sminchisescu C (2007) Support kernel machines for object recognition. In: IEEE international conference on computer vision, pp 1---8
[20]
Kuncheva LI (2002) A theoretical study on six classifier fusion strategies. IEEE Trans Pattern Anal Mach Intell 24(2):281---286
[21]
Kuncheva LI, Bezdek JC, Duin RPW (2001) Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recognition 34(2):299---314
[22]
Lanckriet G, Cristianini N, Bartlett P, Ghaoui L, Jordan M (2004) Learning the kernel matrix with semidefinite programming. J Mach Learn Res 5:27---72
[23]
Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. IEEE Int Conf Comput Vis Pattern Recogn 2:2169---2178
[24]
Lee DS (1995) Theory of classifier combination: the neural network approach. Ph.D. thesis, SUNY at Buffalo
[25]
Lin YY, Liu TL, Fuh CS (2007) Local ensemble kernel learning for object category recognition. In: IEEE international conference on computer vision and pattern recognition, pp 1---8
[26]
Liu W, Tao D (2013) Multiview hessian regularization for image annotation. IEEE Trans Image Process 22(7):2676---2687
[27]
Liu W, Tao D, Cheng J, Tang Y (2014) Multiview hessian discriminative sparse coding for image annotation. Comput Vis Image Underst 118(1):50---60
[28]
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91---110
[29]
Ludwig O, Delgado D, Goncalves V, Nunes U (2009) Trainable classifier-fusion schemes: an application to pedestrian detection. In: International conference on intelligent transportation systems, pp 1---6
[30]
Luo Y, Tao D, Geng B, Xu C, Maybank SJ (2013) Manifold regularized multitask learning for semi-supervised multilabel image classification. IEEE Trans Image Process 22(2):523---536
[31]
Luo Y, Tao D, Xu C, Liu H, Wen Y (2013) Multiview vector-valued manifold regularization for multilabel image classification. IEEE Trans Neural Netw Learn Syst 24(5):709---722
[32]
Matas J, Chum O, Urban M, Pajdla T (2002) Robust wide-baseline stereo from maximally stable extremal regions. Br Mach Vis Conf 1:384---393
[33]
Nilsback ME, Zisserman A (2006) A visual vocabulary for flower classification. In: IEEE international conference on computer vision, pp 1447---1454
[34]
Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971---987
[35]
Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42(3):145---175
[36]
Schuffler P, Fuchs T, Ong C, Roth V, Buhmann J (2010) Computational TMA analysis and cell nucleus classification of renal cell carcinoma. In: 32 annual symposium of the German Pattern Recognition Society, pp 202---211
[37]
Shechtman E, Irani M (2007) Matching local self-similarities across imagesn and videos. In: IEEE international conference on computer vision and pattern recognition, pp 1---8
[38]
Tao D, Jin L (2012) Discriminative information preservation for face recognition. Neurocomputing 91:11---20
[39]
Tao D, Jin L, Liu W, Li X (2013) Hessian regularized support vector machines for mobile image annotation on the cloud. IEEE Trans Multimed 15(4):833---844
[40]
Tulyakov S, Jaeger S, Govindaraju V, Doermann D (2008) Review of classifier combination methods. Springer, Berlin, Hiedelberg
[41]
Ulas A, Duin R, Castellani U, Loog M, Bicego M, Murino V, Bellani M, Cerruti S, Tansella M, Brambilla P (2010) Dissimilarity-based detection of schizophrenia. In: ICPR workshop on pattern recognition challenges in FMRI neuroimaging, pp 32---35
[42]
Varma M, Ray D (2007) Learning the discriminative power-invariance trade-off. In: IEEE international conference on computer vision, pp 1---8
[43]
Varma M, Zisserman A (2005) A statistical approach to texture classification from single images. Image Vis Comput 62(1-2):61---81
[44]
Veeramachaneni K, Yan W, Goebel K, Osadciw L (2007) Improving classifier fusion using particular swarm optimization. In: IEEE symposium on computational intelligence in multicriteria decision making, pp 128---135
[45]
Xia T, Tao D, Mei T, Zhang Y (2010) Multiview spectral embedding. IEEE Trans Syst Man Cybern Part B 40(6):1438---1446
[46]
Xu L, Krzyzak A, Suen CY (1992) Methods for combining multiple classifiers and their applications to handwriting recognition. IEEE Trans Syst Man Cybern Part B 23(3):418---435
[47]
Yang JJ, Li YN, Tian YH, Duan LY, Gao W (2009) Group-sensitive multiple kernel learning for object categorization. In: IEEE international conference on computer vision, pp 436---443
[48]
Yu J, Liu D, Tao D, Seah HS (2012) On combining multiple features for cartoon character retrieval and clip synthesis. IEEE Trans Syst Man Cybern Part B 42(5):1413---1427
[49]
Yu J, Tao D, Rui Y, Cheng J (2013) Pairwise constraints based multiview features fusion for scene classification. Pattern Recognition 46(2):483---496
[50]
Yu J, Tao D, Wang M (2012) Adaptive hypergraph learning and its application in image classification. IEEE Trans Image Process 21(7):3262---3272
[51]
Yu J, Wang M, Tao D (2012) Semisupervised multiview distance metric learning for cartoon synthesis. IEEE Trans Image Process 21(11):4636---4648
[52]
Zhao Z, Liu H (2008) Multi-source feature selection via geometry dependent covariance analysis. J Mach Learn Res Proc Track 4:36---47
[53]
Zhou D, Burges C (2007) Spectral clustering and transductive learning with multiple views. In: International conference on machine learning, pp 1159---1166
[54]
Zhou D, Huang J, Schlkopf B (2006) Learning with hypergraphs: clustering, classification, and embedding. In: Advances in neural information processing systems, pp 1601---1608
[55]
Zien A, Ong CS (2007) Multiclass multiple kernel learning. In: International conference on machine learning, pp 1191---1198
- Evaluating classifier combination in object classification
Recommendations
Building Locally Discriminative Classifier Ensemble Through Classifier Fusion Among Nearest Neighbors
PCM 2016: 17th Pacific-Rim Conference on Advances in Multimedia Information Processing - Volume 9916Many studies on ensemble learning that combines multiple classifiers have shown that, it is an effective technique to improve accuracy and stability of a single classifier. In this paper, we propose a novel discriminative classifier fusion method, which ...
Dynamically building diversified classifier pruning ensembles via canonical correlation analysis
Empirical studies on ensemble learning that combines multiple classifiers have shown that, it is an effective technique to improve accuracy and stability of a single classifier. In this paper, we propose a novel method of dynamically building ...
Classifier combination based on confidence transformation
This paper investigates the effects of confidence transformation in combining multiple classifiers using various combination rules. The combination methods were tested in handwritten digit recognition by combining varying classifier sets. The classifier ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Copyright © Copyright © 2015 Springer-Verlag London.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 01 November 2015
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 09 Nov 2024
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in