Auto feature selection for object detection, can or can't?
Pages 71 - 76
Abstract
This research focuses on developing a system that can retrieve objects from a large image database by exploring the different types of image features. We propose a global representation of object based on the combination of multiple features. After that, we design a novel method for generic object detecting in still images with automatic feature selection. Our method is simple, computationally efficient The main advantage of this method is that it can automatically choose features which are the most suitale for detecting one type of object. We present experimental results for detecting many visual categories including side view car, front view car, bike, motorbike, train, aero plane, horse, sheep, flower and tower. Results clearly demonstrate that the proposed method is robust and produces good detection accuracy rate.
References
[1]
Bay, H., Ess, A., Tuytelaars, T., and Van Gool, L. 2008. Speeded-Up Robust Features (SURF). Comput. Vis. Image Underst. 110, 3 (June), 346--359.
[2]
Coleman, S., Scotney, B., and Kerr, D. 2007. Integrated Edge and Corner Detection. In Image Analysis and Processing, 2007. ICIAP 2007. 14th International Conference on, IEEE, 653--658.
[3]
Csurka, G., Dance, C. R., Fan, L., Willamowski, J., and Bray, C. A. 2004. Visual categorization with bags of keypoints. In In Workshop on Statistical Learning in Computer Vision, ECCV, 1--22.
[4]
Dalal, N., and Triggs, B. 2005. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, IEEE, Washington, DC, USA, vol. 1 of CVPR '05, 886--893 vol. 1.
[5]
Farhadi, A., Endres, I., Hoiem, D., and Forsyth, D. 2009. Describing objects by their attributes. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, IEEE, 1778--1785.
[6]
Ferrari, V., and Zisserman, A. 2007. Learning Visual Attributes. In Advances in Neural Information Processing Systems.
[7]
Grubinger, M., Clough, P., Hanbury, A., and Müller, H. 2008. Advances in Multilingual and Multimodal Information Retrieval. Springer-Verlag, Berlin, Heidelberg, ch. Overview of the ImageCLEFphoto 2007 Photographic Retrieval Task, 433--444.
[8]
Jing, Y., Baluja, S., and Rowley, H. 2007. Canonical image selection from the web. In Proceedings of the 6th ACM international conference on Image and video retrieval, ACM, New York, NY, USA, CIVR '07, 280--287.
[9]
Lampert, C. H., Nickisch, H., and Harmeling, S. 2009. Learning to detect unseen object classes by between-class attribute transfer. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, IEEE, 951--958.
[10]
Lazebnik, S., Schmid, C., and Ponce, J. 2006. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, IEEE, Los Alamitos, CA, USA, vol. 2, 2169--2178.
[11]
Lowe, D. G. 2004. Distinctive Image Features from Scale-Invariant Keypoints. Int. J. Comput. Vision 60, 2 (Nov.), 91--110.
[12]
Marszalek, M., Schmid, C., Harzallah, H., and van de Weijer, J. 2007. Learning Object Representations for Visual Object Class Recognition. Visual Recognition Challange workshop, in conjunction with ICCV.
[13]
Monzo, D., Albiol, A., and Sastre, J. 2008. HOG-EBGM vs. Gabor-EBGM. In Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on, IEEE, 1636--1639.
[14]
Smeulders, A. W. M., Worring, M., Santini, S., Gupta, A., and Jain, R. 2000. Content-based image retrieval at the end of the early years. Pattern Analysis and Machine Intelligence, IEEE Transactions on 22, 12 (Dec.), 1349--1380.
[15]
van de Weijer, J., Schmid, C., Verbeek, J., and Larlus, D. 2009. Learning Color Names for Real-World Applications. Image Processing, IEEE Transactions on 18, 7 (July), 1512--1523.
[16]
Vogel, J., and Schiele, B. 2004. Natural Scene Retrieval Based on a Semantic Modeling Step. In In CIVR.
[17]
Zhu, Z., Lu, H., Hu, J., and Uchimura, K. 2004. Car Detection Based on Multi-Cues Integration. In Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02, IEEE Computer Society, Washington, DC, USA, ICPR '04, 699--702.
Index Terms
- Auto feature selection for object detection, can or can't?
Recommendations
Multispectral Object Detection for Autonomous Vehicles
Thematic Workshops '17: Proceedings of the on Thematic Workshops of ACM Multimedia 2017Recently, researchers have actively conducted studies on mobile robot technologies that involve autonomous driving. To implement an automatic mobile robot (e.g., an automated driving vehicle) in traffic, robustly detecting various types of objects such ...
A feature binding model in computer vision for object detection
AbstractIn this paper, the authors propose the “Feature Binding (FB)” strategy in computer vision, a method combined with the biological visual perception theory. Based on feature subspace, the proposed method refers to the biological model and binds ...
Multi-scale Fusion based Multi-stage Small Object Detection in Aerial Images ∗
EITCE '22: Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer EngineeringIn aerial images, the objects are mostly small. The number of objects is large and the scale is diverse, so it is difficult to extract the features of multiple scale objects at the same time. The location distribution of object in aerial images is ...
Comments
Information & Contributors
Information
Published In
April 2011
158 pages
Copyright © 2011 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
- Comenius University: Comenius University
In-Cooperation
- SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
- EUROGRAPHICS: The European Association for Computer Graphics
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 28 April 2011
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
SCCG '11
Sponsor:
- Comenius University
Acceptance Rates
SCCG '11 Paper Acceptance Rate 20 of 42 submissions, 48%;
Overall Acceptance Rate 67 of 115 submissions, 58%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 76Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 05 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in