Abstract
This paper presents a novel bio-inspired network for image recognition. The HMAX model and the extreme learning machine (ELM) are combined, to construct a five-layer feed-forward network: S1-C1-S2-C2-H. The previous four layers, originating from HMAX, provide robust feature representation of specific object, and the feature classification stage at the H layer is implemented with ELM. The HMAX model simulates the hierarchical processing mechanism in primate visual cortex, to calculate feature representation. As a biological learning algorithm for SLFNs, ELM learns much faster with good generalization, and performs well in classification applications. Our experimental results show effective accuracy performance with fast learning speed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Mohan, A., Papageorgiou, C., Poggio, T.: Example-based object detection in images by components. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 349–361 (2001)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Serre, T., Wolf, L., Poggio, T.: Object recognition with features inspired by visual cortex. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR 2005, pp. 994–1000. IEEE (2005)
Mutch, J., Lowe, D.G.: Object class recognition and localization using sparse features with limited receptive fields. International Journal of Computer Vision 80, 45–57 (2008)
Riesenhuber, M., Poggio, T.: Hierarchical models of object recognition in cortex. Nature Neuroscience 2, 1019–1025 (1999)
Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., Poggio, T.: Robust object recognition with cortex-like mechanisms. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 411–426 (2007)
Pinto, N., Barhomi, Y., Cox, D.D., DiCarlo, J.J.: Comparing state-of-the-art visual features on invariant object recognition tasks. In: 2011 IEEE workshop on Applications of computer vision (WACV), pp. 463–470. IEEE (2011)
Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, pp. 985–990. IEEE (2004)
Huang, G.-B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42, 513–529 (2012)
Zong, W., Huang, G.-B.: Face recognition based on extreme learning machine. Neurocomputing 74, 2541–2551 (2011)
Huang, Y., Huang, K., Tao, D., Tan, T., Li, X.: Enhanced biologically inspired model for object recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 41, 1668–1680 (2011)
Theriault, C., Thome, N., Cord, M.: Extended coding and pooling in the HMAX model. IEEE Transactions on Image Processing 22, 764–777 (2013)
Huang, G.-B., Chen, L.: Enhanced random search based incremental extreme learning machine. Neurocomputing 71, 3460–3468 (2008)
Hung, C.P., Kreiman, G., Poggio, T., DiCarlo, J.J.: Fast readout of object identity from macaque inferior temporal cortex. Science 310, 863–866 (2005)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2169–2178. IEEE (2006)
Li, P., Ma, J., Gao, S.: Actions in still web images: Visualization, detection and retrieval. In: Wang, H., Li, S., Oyama, S., Hu, X., Qian, T. (eds.) WAIM 2011. LNCS, vol. 6897, pp. 302–313. Springer, Heidelberg (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhang, L., Zhang, Y., Li, P. (2015). A Novel Bio-inspired Image Recognition Network with Extreme Learning Machine. In: Cao, J., Mao, K., Cambria, E., Man, Z., Toh, KA. (eds) Proceedings of ELM-2014 Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-319-14063-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-14063-6_12
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14062-9
Online ISBN: 978-3-319-14063-6
eBook Packages: EngineeringEngineering (R0)