Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Pattern Analysis and Applications
Published in: Pattern Analysis and Applications 4/2017

11-02-2017 | Short Paper

Overlapping area hyperspheres for kernel-based similarity method

Authors: Alya Slimene, Ezzeddine Zagrouba

Published in: Pattern Analysis and Applications | Issue 4/2017

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Measuring similarity between sets of objects is a key step in a wide areas of machine learning. Popular examples include general classification framework and numerous applications in computer vision. In this paper, we propose a kernel-based similarity method which is inspired from an interesting biological behavior of trees and induced mathematically by formulating it as a quadratic optimization problem in a reproducing kernel Hilbert space (RKHS). The proposed method is compared to the maximum mean discrepancy, a recent and challenging kernel similarity method. We conduct and present several numerical experiments on synthetic data as well as real-word image data. The proposed method yields favorable performances in terms of classification performances in the context of supervised classification tasks on the challenging Caltech101 dataset and other datasets such as USPS and ETH80. Furthermore, the efficiency of the proposed method in the context of image segmentation through unsupervised clustering of superpixels has been also asserted.
previous article Synergies between texture features: an abstract feature based framework for meningioma subtypes classification
next article A new descriptor for image matching based on bionic principles

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Ackermann N (2005) A cauchy-schwarz type inequality for bilinear integrals on positive measures. Proc Am Math Soc 133(9):2647–2656CrossRefMATHMathSciNet
2.
3.
4.
5.
Belongie S, Malik J, Puzicha J (2002) Shape matching and object recognition using shape contexts. IEEE Trans Pattern Anal Mach Intell 24:509–522CrossRef
6.
Berg AC, Malik J (2001) Geometric blur for template matching. In: IEEE conference on computer vision and pattern recognition, pp 607–614
7.
Borgwardt KM, Gretton A, Rasch MJ, Kriegel H-P, Schölkopf B, Smola A (2007) A kernel method for the two sample problem. Adv Neural Inf Process Syst 19:513–520
8.
Carli A, Castellani U, Bicego M, Murino V (2010) Dissimilarity-based representation for local parts. In: International workshop on cognitive information processing (CIP), pp 299–303
9.
Cevikalp H (2010) Semi-supervised distance metric learning by quadratic programming. In: International conference on pattern recognition, pp 3352–3355
10.
Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:1–27CrossRef
11.
Chen P, Fan RE, Lin C (2005) Training support vector machines via smo-type decomposition methods. In: International conference on algorithmic learning theory
12.
Cheplygina V, Tax DMJ, Loog M (2015) On classification with bags, groups and sets. Pattern Recogn Lett 59(1):11–17CrossRef
13.
Coen MH, Ansari M, Fillmore N (2011) Learning from spatial overlap. In: AAAI conference on artificial intelligence, 2011
14.
Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619CrossRef
15.
Crosland MP (1978) Gay-Lussac: scientist and bourgeois. Cambridge University Press, CambridgeCrossRef
16.
Delos V, Teissandier D (2015) Minkowski sum of polytopes defined by their vertices. J Appl Math Phys 3(1):62–67CrossRef
17.
Diu M, Gangeh M, Kamel MS (2013) Unsupervised visual changepoint detection using maximum mean discrepancy. In: Image analysis and recognition, 2013
18.
Diu M (2013) Image analysis applications of the maximum mean discrepancy distance measure, PhD thesis, University of Waterloo, 2013
19.
Doménech JL, Gil-Pérez D, Gras-Martí A, Guisasola J, Martínez-Torregrosa J, Salinas J, Trumper R, Valdés P, Vilches A (2007) Teaching of energy issues: a debate proposal for a global reorientation. Sci Educ 16(1):43–64CrossRef
20.
Dueck D, Frey BJ (2007) Non-metric affinity propagation for unsupervised image categorization. In: International conference on computer vision, 2007, pp 1–8
21.
Farid H, Simoncelli EP (2004) Differentiation of discrete multidimensional signals. IEEE Trans Image Process 13(4):496–508CrossRefMathSciNet
22.
Galego R, Ferreira R, Bernardino A, Grossmann E, Gaspar J (2013) Topological auto-calibration of central imaging sensors. In: Pattern recognition and image analysis, 2013, pp 476–483
23.
24.
25.
Goodrich B, Albrecht D, Tischer P (2009) Algorithms for the computation of reduced Convex Hulls. In: Australasian joint conference on advances in artificial intelligence,
26.
Grauman K, Darrell T (2007) The pyramid match kernel: efficient learning with sets of features. J Mach Learn Res 8:725–760MATH
27.
Gretton A, Borgwardt KM, Rasch MJ, Schölkopf B, Smola A (2012) A kernel two-sample test. J Mach Learn Res 13:723–773MATHMathSciNet
28.
Hafiz AM, Bhat GM (2014) Handwritten digit recognition using slope detail features. Int J Comput Appl 93(5):14–19
29.
Hans A (2009) In resonance with nature: holistic healing for plants and land. Floris Books, Edinburgh
30.
Hull JJ (1994) A database for handwritten text recognition research. IEEE Trans Pattern Anal Mach Intell 16(5):550–554CrossRef
31.
Jacobs DW, Weinshall D, Gdalyahu Y (2000) Classification with nonmetric distances: image retrieval and class representation. IEEE Trans Pattern Anal Mach Intell 22(6):583–600CrossRef
32.
Jones WP, Furnas GW (1987) Pictures of relevance: a geometric analysis of similarity measures. J Am Soc Inf Sci 38(6):420–442CrossRef
33.
Kim B, Pineau J (2013) Maximum mean discrepancy imitation learning. Robot Sci Syst
34.
Kinnunen T, Li H (2010) An overview of text-independent speaker recognition: from features to supervectors. Speech Commun 52(1):12–40CrossRef
35.
Klippel A, Weaver C (2008) Analyzing behavioral similarity measures in linguistic and non-linguistic conceptualization of spatial information and the question of individual differences. In: Workshop on information semantics and its implications for geographical analysis
36.
Kondor R, Jebara T (2003) A kernel between sets of vectors. In: International conference on machine learning
37.
Kumar P, Mitchell JSB, Yildirim EA (2003) Approximate minimum enclosing balls in high dimensions using core-sets. J Exp Alg 8:1MATHMathSciNet
38.
Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: IEEE conference on computer vision and pattern recognition
39.
Leibe, B, Schiele B (2003) Analyzing appearance and contour based methods for object categorization. In: IEEE conference on computer vision and pattern recognition
40.
41.
Lin YY, Liu TL (2011) Multiple kernel learning for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 33:1–14CrossRef
42.
Linnett JW (1942) The relation between potential energy and interatomic distance in some diatomic molecules. Trans Faraday Soc 38:1–9CrossRef
43.
Liu H, Ding X (2005) Handwritten character recognition using gradient feature and quadratic classifier with multiple discrimination schemes. In: International conference on document analysis and recognition, pp 19–23
44.
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110CrossRef
45.
46.
Martin D, Fowlkes C, Malik J Tal D (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: International conference on computer vision
47.
Mira J, Sandoval F (1995) From natural to artificial neural computation. In: International workshop on artificial neural networks
48.
Misra G, Golshan B, Terzi E (2012) A framework for evaluating the smoothness of data-mining results. In: Joint European conference on machine learning and knowledge discovery in databases
49.
Moon TK (1996) Similarity methods in signal processing. IEEE Trans Signal Process 44(4):827–833CrossRef
50.
51.
Muandet K, Fukumizu K, Dinuzzo F, Schölkopf B (2012) Learning from distributions via support measure machines. In: Advances in neural information processing systems, pp 10–18
52.
53.
Pekalska E, Duin RPW (2001) Automatic pattern recognition by similarity representations - a novel approach. Electron Lett 37:159–160CrossRef
54.
Piciarelli C, Micheloni C, Foresti GL (2008) Trajectory-based anomalous event detection. IEEE Trans Circuits Syst Video Technol 18(11):1544–1554CrossRef
55.
Plat JC (1998) Fast training of support vector machines using sequential minimal optimization. J Mach Learn Res 1889–1918
56.
Rolle K (2015) Heat and mass transfer. Cengage Learning, Ohio, USA
57.
Schneider R (2013) Convex bodies: The Brunn–Minkowski theory. Cambridge University Press, CambridgeCrossRefMATH
58.
Schölkopf, B (2001) The kernel trick for distances. In Advances in neural information processing systems
59.
Sebe N, Tian Q, Lew MS, Huang TS (2008) Guest editorial: Similarity matching in computer vision and multimedia. Comput Vis Image Underst 110(3):309–311CrossRef
60.
Seidenari L, Serra G, Bagdanov AD, Del Bimbo A (2014) Local pyramidal descriptors for image recognition. IEEE Trans Pattern Anal Mach Intell 36(5):1033–1040CrossRef
61.
Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905CrossRef
62.
63.
Slimene A, Zagrouba E (2013) Kernel maximum mean discrepancy for region merging approach. In: Computer analysis of images and patterns, pp 475–482
64.
Stark M, Schiele B (2007) How good are local features for classes of geometric objects. In: IEEE international conference on computer vision
65.
Sun S (2013) A survey of multi-view machine learning. Neural Comput Appl 23(7–8):2031–2038CrossRef
66.
67.
Torki M, Elgammal A (2010) Putting local features on a manifold. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 1743–1750
68.
Wang B, Sung KK, Ng TK (2002) The localized consistency principle for image matching under non-uniform illumination variation and affine distortion. In: European Conference on Computer Vision, pp 205–219
69.
Wang J, Sang N, Wang Z, Gao C (2016) Similarity learning with top-heavy ranking loss for person re-identification. IEEE Signal Process Lett 23(1):84–88CrossRef
70.
Weibel C (2007) Minkowski sums of polytopes: combinatorics and computation, PhD thesis, Lcole polytechnique fdrale de Lausanne (EPFL)
71.
Williams C, Seeger M (2001) Using the Nystrom method to speed up kernel machines. In: Advances in Neural Information Processing Systems, pp 682–688
72.
Xiong H, Chen XW (2006) Kernel-based distance metric learning for microarray data classification. BMC Bioinformatics 7(1):299CrossRefMathSciNet
73.
Z H, Berg AC, Maire M, Malik J (2006) SVM-KNN: discriminative nearest neighbor classification for visual category recognition. In: IEEE conference on computer vision and pattern recognition, pp 2126–2136
74.
Zhu X, Suk HI, Shen D (2014) Matrix-similarity based loss function and feature selection for Alzheimer’s disease diagnosis. In: IEEE conference on computer vision and pattern recognition, pp 3089–3096
Metadata
Title
Overlapping area hyperspheres for kernel-based similarity method
Authors
Alya Slimene
Ezzeddine Zagrouba
Publication date
11-02-2017
Publisher
Springer London
Published in
Pattern Analysis and Applications / Issue 4/2017
Print ISSN: 1433-7541
Electronic ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-017-0604-0

Other articles of this Issue 4/2017

Pattern Analysis and Applications 4/2017 Go to the issue

Survey

Computerised approaches for the detection of diabetic retinopathy using retinal fundus images: a survey

Industrial and Commercial Application

New dissimilarity measures for image phylogeny reconstruction

Original Article

Robust partial fingerprint recognition using wavelet SIFT descriptors

Theoretical Advances

Robust embedded projective nonnegative matrix factorization for image analysis and feature extraction

Theoretical Advances

Double-fold localized multiple matrix learning machine with Universum

Short Paper

Biharmonic density estimate: a scale-space descriptor for 3-D deformable surfaces

Premium Partner

    Image Credits