Article

Representing shape with a spatial pyramid kernel

Authors:

Andrew Zisserman,

Xavier MunozAuthors Info & Claims

CIVR '07: Proceedings of the 6th ACM international conference on Image and video retrieval

Pages 401 - 408

https://doi.org/10.1145/1282280.1282340

Published: 09 July 2007 Publication History

Abstract

The objective of this paper is classifying images by the object categories they contain, for example motorbikes or dolphins. There are three areas of novelty. First, we introduce a descriptor that represents local image shape and its spatial layout, together with a spatial pyramid kernel. These are designed so that the shape correspondence between two images can be measured by the distance between their descriptors using the kernel. Second, we generalize the spatial pyramid kernel, and learn its level weighting parameters (on a validation set). This significantly improves classification performance. Third, we show that shape and appearance kernels may be combined (again by learning parameters on a validation set).

Results are reported for classification on Caltech-101 and retrieval on the TRECVID 2006 data sets. For Caltech-101 it is shown that the class specific optimization that we introduce exceeds the state of the art performance by more than 10%.

References

[1]

F. Bach, G. Lanckriet, and M. Jordan. Multiple kernel learning, conic duality, and the smo algorithm. In Proc. ICML, 2004.

Digital Library

[2]

A Bissacco, M. H. Yang, and S. Soatto. Detecting humans via their pose. In NIPS, 2006.

Digital Library

[3]

G. Borgefors. Hierarchical chamfer matching: A parametric edge matching algorithm. IEEE PAMI, 10(6):849--865, 1988.

Digital Library

[4]

A. Bosch, A. Zisserman, and X. Muoz. Scene classification via plsa. In Proc. ECCV, 2006.

Digital Library

[5]

C. Chang and C. Lin. LIBSVM: a library for support vector machines, 2001. Software available at urlhttp://www.csie.ntu.edu.tw/cjlin/libsvm.

[6]

G. Csurka, C. Bray, C. Dance, and L. Fan. Visual categorization with bags of keypoints. In Workshop on Statistical Learning in Computer Vision, ECCV, 2004.

[7]

N. Dalal and B Triggs. Histogram of oriented gradients for human detection. In Proc. CVPR, 2005.

Digital Library

[8]

L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In IEEE CVPR Workshop of Generative Model Based Vision, 2004.

Digital Library

[9]

P. F. Felzenszwalb. Learning models for object recognition. In Proc. CVPR, 2001.

[10]

D. Gavrila and V. Philomin. Real-time object detection for "smart" vehicles. In Proc. ICCV, 1999.

[11]

K. Grauman and T. Darrell. The pyramid match kernel: Discriminative classification with sets of image features. In Proc. ICCV, 2005.

Digital Library

[12]

D. Haussler. Convolution kernels on discrete structures. Technical Report UCS-CRL-99-10, 1999.

[13]

A. K. Jain and K. Karu. Learning texture discrimination masks. IEEE PAMI, 18:195--205, Feb 1996.

Digital Library

[14]

S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Proc. CVPR, 2006.

Digital Library

[15]

D. Lewis, T. Jebara, and W. Noble. Nonstationarly kernel combination. In Proc. ICML, 2006.

Digital Library

[16]

T. Lindeberg and J. Gårding. Shape-adapted smoothing in estimation of 3-D depth cues from affine distortions of local 2-D brightness structure. In Proc. ECCV, 1994.

Digital Library

[17]

D. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91--110, 2004.

Digital Library

[18]

J. Mutch and D. Lowe. Multiclass object recognition using sparse, localized features. In Proc. CVPR, 2006.

Digital Library

[19]

A. Opelt, A. Pinz, and A. Zisserman. Incremental learning of object detectors using a visual shape alphabet. In Proc. CVPR, 2006.

Digital Library

[20]

E. Seemann, B. Leibe, K. Mikolajczyk, and B. Schiele. An evaluation of local shape-based features for pedestrian detection. In Proc. BMVC., 2005.

Digital Library

[21]

J. Shotton, A. Blake, and R. Cipolloa. Contour-based learning for object detection. In Proc. ICCV, 2005.

Digital Library

[22]

B. Stenger, A. Thayananthan, P. H. S. Torr, and R. Cipolla. Filtering using a tree-based estimator. In ICCV, 2003.

Digital Library

[23]

M. Szummer and R. W. Picard. Indoor-outdoor image classification. In ICCV Workshop on Content-based Access of Image and Video Databases, 1998.

Digital Library

[24]

J. Thureson and S. Carlsson. Appearance based qualitative image description for object class recognition. In Proc. ECCV, 2004.

[25]

G. Wang, Y. Zhang, and L. Fei-Fei. Using dependent regions for object categorization in a generative framework. In Proc. CVPR, 2006.

Digital Library

[26]

H. Zhang, A. Berg, M. Maire, and J. Malik. SVM-KNN: Discriminative nearest neighbor classification for visual category recognition. In Proc. CVPR, 2006.

Digital Library

[27]

J. Zhang, M. Marszałek, S. Lazebnik, and C. Schmid. Local features and kernels for classification of texture and object categories: a comprehensive study. IJCV, 2007.

Digital Library

Cited By

Alinsaif S(2024)COVID-19 Image Classification: A Comparative Performance Analysis of Hand-Crafted vs. Deep FeaturesComputation10.3390/computation1204006612:4(66)Online publication date: 30-Mar-2024
https://doi.org/10.3390/computation12040066
Alphonse SAbinaya SKumar N(2024)Pain assessment from facial expression images utilizing Statistical Frei-Chen Mask (SFCM)-based features and DenseNetJournal of Cloud Computing10.1186/s13677-024-00706-913:1Online publication date: 23-Sep-2024
https://doi.org/10.1186/s13677-024-00706-9
Nimbhore PTiwari RHazra TYadav M(2024) Classification of cotton crop disease using hybrid model and MDFC feature extraction method Journal of Phytopathology10.1111/jph.13324172:4Online publication date: 2-Aug-2024
https://doi.org/10.1111/jph.13324
Show More Cited By

Index Terms

Representing shape with a spatial pyramid kernel
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding

Recommendations

3D spatial pyramid: descriptors generation from point clouds for indoor scene classification

Traditionally, the indoor scene classification problem has been approached from a 2D image recognition point of view. In most visual scene classification systems, a descriptor for the input image is generated to obtain a suitable representation that ...
Image Classification Using Spatial Difference Descriptor Under Spatial Pyramid Matching Framework
MMM 2016: Proceedings, Part I, of the 22nd International Conference on MultiMedia Modeling - Volume 9516

Spatial pyramid matching SPM model is an extension of the bag-of-visual words BoW model for local feature encoding. It firstly partitions the image into increasingly fine sub-regions, and then concatenates the histograms within each sub-region. However, ...
Plant species identification using Elliptic Fourier leaf shape analysis

Elliptic Fourier (EF) and discriminant analyses were used to identify young soybean (Glycine max (L.) merrill), sunflower (Helianthus pumilus), redroot pigweed (Amaranthus retroflexus) and velvetleaf (Abutilon theophrasti Medicus) plants, based on leaf ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CIVR '07: Proceedings of the 6th ACM international conference on Image and video retrieval

July 2007

655 pages

ISBN:9781595937339

DOI:10.1145/1282280

General Chairs:
Nicu Sebe
Univ. of Amsterdam, The Netherlands
,
Marcel Worring
Univ. of Amsterdam, The Netherlands

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

In-Cooperation

SIGIR: ACM Special Interest Group on Information Retrieval

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 July 2007

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

CIVR07

Sponsor:

SIGMM

CIVR07: International Conference on Image and Video Retrieval 2007

July 9 - 11, 2007

Amsterdam, The Netherlands

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1,069
Total Citations
View Citations
2,982
Total Downloads

Downloads (Last 12 months)41
Downloads (Last 6 weeks)6

Reflects downloads up to 25 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Alinsaif S(2024)COVID-19 Image Classification: A Comparative Performance Analysis of Hand-Crafted vs. Deep FeaturesComputation10.3390/computation1204006612:4(66)Online publication date: 30-Mar-2024
https://doi.org/10.3390/computation12040066
Alphonse SAbinaya SKumar N(2024)Pain assessment from facial expression images utilizing Statistical Frei-Chen Mask (SFCM)-based features and DenseNetJournal of Cloud Computing10.1186/s13677-024-00706-913:1Online publication date: 23-Sep-2024
https://doi.org/10.1186/s13677-024-00706-9
Nimbhore PTiwari RHazra TYadav M(2024) Classification of cotton crop disease using hybrid model and MDFC feature extraction method Journal of Phytopathology10.1111/jph.13324172:4Online publication date: 2-Aug-2024
https://doi.org/10.1111/jph.13324
Liu YWu YZhang SLiu LWu MCheng M(2024)Revisiting Computer-Aided Tuberculosis DiagnosisIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.333082546:4(2316-2332)Online publication date: Apr-2024
https://doi.org/10.1109/TPAMI.2023.3330825
Zhao JXu Y(2024)Multiview Structural Large Margin Classifier and its Safe Acceleration StrategyIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.329054035:11(15944-15955)Online publication date: Nov-2024
https://doi.org/10.1109/TNNLS.2023.3290540
Li ZQian YZang YZhao JSheng GJiang X(2024)Optical Partial Discharge Detection and Diagnosis Method Based on PHOG FeaturesIEEE Transactions on Dielectrics and Electrical Insulation10.1109/TDEI.2023.333069731:6(3040-3048)Online publication date: Dec-2024
https://doi.org/10.1109/TDEI.2023.3330697
Chellakh HMoussaoui AAttia A(2024)Improved Binarized Statistical Image Features for MRI Brain Tumor Identification and Classification2024 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)10.1109/ICT-DM62768.2024.10798938(1-7)Online publication date: 19-Nov-2024
https://doi.org/10.1109/ICT-DM62768.2024.10798938
Pandey MMishra RKhare A(2024)A Shape-Based Feature Descriptor for Multi-View Human Activity Recognition2024 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI)10.1109/CVMI61877.2024.10782822(1-6)Online publication date: 19-Oct-2024
https://doi.org/10.1109/CVMI61877.2024.10782822
Wang SZhao XGuo HQi FQiao YWang C(2024)Fusion model of gray level co-occurrence matrix and convolutional neural network faced for histopathological imagesReview of Scientific Instruments10.1063/5.021641795:10Online publication date: 25-Oct-2024
https://doi.org/10.1063/5.0216417
Ashtari-Majlan MDehshibi MMasip D(2024)Glaucoma diagnosis in the era of deep learningExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124888256:COnline publication date: 5-Dec-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124888
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten