Abstract
Semantic image annotation can be viewed as a mapping procedure from image features to semantic labels, by the steps of image feature extraction and image-semantic mapping. The features can be low-level visual features, such as color, texture, shape, etc., and the semantic labels can be related to the knowledge of human on the image understanding. However, these linear representations are insufficient to describe the complex natural scene. In this paper, we study currently existing visual models that are able to imitate the way the human visual system acts for the tasks of object recognition and scene interpretation. Therefore, it is expected to bring a better understanding to the image visual content in human cortex will. In the experiments, there are three state-of-the-art visual models are investigated for the application of automatic image annotation. The results demonstrate that with our proposed strategy, the annotation accuracy is improved comparing to the most used low-level linear representation features.
Chapter PDF
Similar content being viewed by others
References
Karklin, Y., Levicki, M.S.: Emergence of Complex Cell Properties by Learning to Generalize in Natural Scenes. Nature 457, 83–86 (2009)
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)
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)
Gabor, D.: Theory of Communication. J. IEE 93, 429–459 (1946)
Field, D.: What is the goal of sensory coding? Neural Computation 6, 559–601 (1994)
Hubel, D.H., Wiesel, T.N.: Receptive Fields, Binocular Interaction and Functional Architecture in The Cat’s Visual Cortex. J. Physiology 160, 106–154 (1962)
Cavanaugh, J.R., Bair, W., Movshon, J.A.: Nature and Interaction of Signals from The Receptive Field Center and Surround in Macaque V1 Neurons. J. Neurophysiology 88, 2530–2546 (2002)
Heeger, D.J., Simoncelli, E.P., Movshon, J.A.: Computational Methods of Cortical Visual Processing. Proc. Natl Acad. Sci., 623–627 (1996)
Hyvarinen, A., Hoyer, P.: A Two-layer Sparse Coding Model Learns Simple and Complex Cell Receptive Fields and Topography from Natural Images. Vision Res. 42, 241–2423 (2001)
Hubel, D., Wiesel, T.: Receptive Fields and Functional Architecture in Two Nonstriate Visual Areas of the Cat. J. Neurophysiology 28, 22–289 (1965)
Bruce, C., Desimone, R., Gross, C.: Visual Properties of Neurons in a Polysensory Area in the Superior Temporal Sulcus of the Macaque. J. Neurophysiology 46, 369–384 (1981)
Comon, P.: Independent Component Analysis, a New Concept? Signal Processing 36, 287–314 (1994)
Ullman, S., Vidal-Naquet, M., Sali, E.: Visual Features of Intermediate Complexity and Their Use in Classification. Nature Neuroscience 5, 68–687 (2002)
Karklin, Y., Levicki, M.S.: A Hierarchical Bayesian Model for Learning Nonlinear Statistical Regularities in Nonstationary Natural Signals. Neural Computation 17, 397–423 (2005)
Karklin, Y.: Hierarchical Statistical Models of Computation in the Visual Cortex. PhD thesis, Carnegie Mellon University (2007)
Perlibakas, V.: Distance Measures for PCA-based Face Recognition. Pattern Recognition Letters 25, 711–724 (2004)
Hu, R.K., Shao, S., Guo, P.: Investigating Visual Feature Extraction Methods for Image Annotation. In: Proceedings of 2009 IEEE International Conference on Systems, Man, and Cybernetics, pp. 3211–3216 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guo, P., Wan, T., Ma, J. (2011). Experimental Studies of Visual Models in Automatic Image Annotation. In: Jacko, J.A. (eds) Human-Computer Interaction. Design and Development Approaches. HCI 2011. Lecture Notes in Computer Science, vol 6761. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21602-2_61
Download citation
DOI: https://doi.org/10.1007/978-3-642-21602-2_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21601-5
Online ISBN: 978-3-642-21602-2
eBook Packages: Computer ScienceComputer Science (R0)