Simone Palazzo
ABSTRACT
Kernel descriptors consist in finite-dimensional vectors extracted from image patches and designed in such a way that the dot product approximates a nonlinear kernel, whose projection feature space would be high-dimensional. Recently, they have been successfully used for fine-gradined object recogntion, and in this work we study the application of two such descriptors, called EMK and KDES (respectively designed as a kernelized generalization of the common bag-of-words and histogram-of-gradient approaches) to the MAED 2014 Fish Classification task, consisting of about 50,000 underwater images from 10 fish species.
- S. Belongie, J. Malik, and J. Puzicha. Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(4):509--522, Apr. 2002. Google Scholar
Digital Library
- A. Berg, T. Berg, and J. Malik. Shape Matching and Object Recognition using Low Distortion Correspondences. In Computer Vision and Pattern Recognition, pages 26--33, 2005. Google ScholarDigital Library
- L. Bo, X. Ren, and D. Fox. Kernel descriptors for visual recognition. In Neural Information Processing Systems, 2010.Google Scholar
- L. Bo and C. Sminchisescu. Efficient Match Kernels between Sets of Features for Visual Recognition. In Y. Bengio, D. Schuurmans, J. La_erty, C. K. I. Williams, and A. Culotta, editors, Neural Information Processing Systems, volume 2, pages 135--143. Citeseer, 2009.Google Scholar
- C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and . . . , 2:1--39, 2011. Google ScholarDigital Library
- O. Chapelle, P. Haffner, and V. Vapnik. SVMs for Histogram-Based Image Classification. IEEE Transactions On Neural Networks, 10(5):1055--1064, 1999. Google ScholarDigital Library
- N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. In Computer Vision and Pattern Recognition (CVPR'05), volume 1, pages 886--893. Ieee, 2005. Google ScholarDigital Library
- P. Felzenszwalb, D. McAllester, and D. Ramanan. A discriminatively trained, multiscale, deformable part model. In Computer Vision and Pattern Recognition, pages 1--8, 2008.Google ScholarCross Ref
- K. Grauman and T. Darrell. The Pyramid Match Kernel : Efficient Learning with Sets of Features. Journal of Machine Learning Research, 8:725--760, 2007. Google ScholarDigital Library
- D. Haussler. Convolution Kernels on Discrete Structures. Technical report, 1999.Google Scholar
- R. Kondor and T. Jebara. A Kernel Between Sets of Vectors. In International Conference on Machine Learning, volume 20, 2003.Google Scholar
- S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Computer Vision and Pattern Recognition, volume 2, pages 2169--2178, 2006. Google ScholarDigital Library
- D. G. Lowe. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 60(2):91--110, Nov. 2004. Google ScholarDigital Library
- S. Lyu. Mercer Kernels for object recognition with local features. In Computer Vision and Pattern Recognition, volume 2, pages 223--229, 2005. Google ScholarDigital Library
- K. Mikolajczyk and C. Schmid. Indexing based on scale invariant interest points. In International Conference on Computer Vision, volume 1, pages 525--531, 2001.Google ScholarCross Ref
- P. J. Moreno, P. P. Ho, and N. Vasconcelos. A Kullback-Leibler divergence based kernel for SVM classification in multimedia applications. In Neural Information Processing Systems, volume 16, pages 1385--1393. MIT Press, 2003.Google Scholar
- D. Roobaert and M. V. Hulle. View-based 3D object recognition with support vector machines. In IEEE Signal Processing Society Workshop, pages 77--84, 1999.Google ScholarCross Ref
- A. Shashua and T. Hazan. Algebraic Set Kernels with Applications to Inference over Local Image Representations. In Neural Information Processing Systems, pages 1257--1264. MIT Press, 2005.Google Scholar
- J. Sivic and A. Zisserman. Video Google: a text retrieval approach to object matching in videos. In International Conference on Computer Vision, number Iccv, pages 1470--1477. Ieee, 2003. Google ScholarDigital Library
- A. Vedaldi and B. Fulkerson. VLFeat - An open and portable library of computer vision algorithms. In International conference on Multimedia, volume 3, pages 1469--1472. ACM, 2010. Google ScholarDigital Library
- C. Wallraven, B. Caputo, and A. Graf. Recognition with local features: the kernel recipe. In International Conference on Computer Vision, 2003. Google ScholarDigital Library
- L. Wolf and A. Shashua. Learning over Sets using Kernel Principal Angles. Journal of Machine Learning Research, 4:913--931, 2003. Google ScholarDigital Library
Index Terms
- Fish Species Identification in Real-Life Underwater Images
Recommendations
Automatic fish classification for underwater species behavior understanding
ARTEMIS '10: Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streamsThe aim of this work is to propose an automatic fish classification system that operates in the natural underwater environment to assist marine biologists in understanding subehavior. Fish classification is performed by combining two types of features: ...
Real-time underwater sorting of edible fish species
Common carp (Cyprinus carpio), St. Peter's fish (Oreochromis sp.) and grey mullet (Mugil cephalus), were sorted according to species while swimming in pond water containing algae and suspended sediments. Fish images were acquired by a computer vision ...
A rule-based event detection system for real-life underwater domain
Understanding and analyzing fish behaviour is a fundamental task for biologists that study marine ecosystems because the changes in animal behaviour reflect environmental conditions such as pollution and climate change. To support investigators in ...
Comments