ABSTRACT This paper mainly focuses on the issues about generic multi-scale object perception for detection or recognition. A novel computational model in visually-feature space is presented for scene & object representation to purse the underlying textural manifold statistically in nonparametric manner. The associative method approximately makes perceptual hierarchy in human-vision biologically coherency in specific quad-tree-pyramid structure, and the appropriate scale-value of different objects can automatically be selected by evaluating from well-defined scale function without any priori knowledge. The sufficient experiments truly demonstrate the effectiveness of scale determination in textural manifold with object localization rapidly.
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K. Wu, Z. Xie and J. Gao, "Multi-Scale Object Perception with Embedding Textural Space," International Journal of Intelligence Science, Vol. 2 No. 2, 2012, pp. 32-39. doi: 10.4236/ijis.2012.22005.
 Y. Wang and S.-C. Zhu, “Perceptual Scale-Space and Its Applications,” International Journal of Computer Vision, Vol. 80, No. 1, 2008, pp. 143-165.
 Y. S. Kang and H. Nagarashi, “Depth Perception from a 2D Natural Scene Using Scale Variation of Texture Patterns,” IEICE Transactions on Information and Systems, Vol. 3, 2006, pp. 1294-1298.
 J. M. Henderson, “Human Gaze Control during Real- World Scene Perception,” Trends in Cognitive Sciences, Vol. 7, No. 11, 2003, pp. 498-504.
 J. Vogel and B. Schiele, “Semantic Modeling of Natural Scenes for Content-Based Image Retrieval,” International Journal of Computer Vision, Vol. 72, No. 2, 2007, pp. 133-157. doi:10.1007/s11263-006-8614-1
 M. M. Silva, J. A. Groeger, et al., “Attention-Memory Interactions in Scene Perception,” Spatial Vision, Vol. 19, No. 1, 2006, pp. 9-19. doi:10.1163/156856806775009223
 B. Reimer, L. A. D’Ambrosio, et al., “Behavior Differences in Drivers with Attention Deficit Hyperactivity Disorder: The Driving Behavior Questionnaire,” Accident, Analysis and Prevention, Vol. 37, No. 6, 2005, pp. 996- 1004. doi:10.1016/j.aap.2005.05.002
 D. Le and E. Izquierdo, “Global-to-Local Oriented Rapid Scene Perception,” 9th International Workshop on Image Analysis for Multimedia Interactive Services, Klagenfurt, 7-9 May 2008, pp. 155-158.
 S. Lazebnik, C. Schmid, et al., “Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, 9 October 2006, pp. 2169-2178.
 M. Gheiratmand, H. Soltanian-Zadeh, et al., “Towards an Inclusive Computational Model of Visual Cortex,” 8th IEEE International Conference on Bioinformatics and BioEngineering, Athens, 8-10 October 2008, pp. 1-5.
 S. Grossberg, “Towards a Unified Theory of Neocortex: Laminar Cortical Circuits for Vision and Cognition,” Computational Neuroscience: Theoretical Insights into Brain Function, Vol. 165, 2007, pp. 79-104.
 A. Oliva and A. Torralba, “Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope,” International Journal of Computer Vision, Vol. 42, No. 3, 2001, pp. 145-175. doi:10.1023/A:1011139631724
 T. Yuehua, M. Skubic, et al., “Performance Evaluation of SIFT-Based Descriptors for Object Recognition,” Pro- ceedings International Multi Conference of Engineering and Computer Scientists, Hong Kong, 17-19 March 2010, pp. 978-988.
 B. Johnson and Z. Xie, “Unsupervised Image Segmentation Evaluation and Refinement Using a Multi-Scale Approach,” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 66, No. 4, 2011, pp. 473-483.
 N. Bianchi-Berthouze, “Subjective Perception of Natural Scenes: The Role of Color,” Proceedings of SPIE—The International Society for Optical Engineering, Santa Clara, 21 January 2003, pp. 1-13.
 A. H. Assadi, “Perceptual Geometry of Space and Form: Visual Perception of Natural Scenes and Their Virtual Representation,” Proceedings of SPIE—The International Society for Optical Engineering, Shanghai, 29 July 2001, pp. 59-72.
 I. Gurevich, “The Descriptive Techniques for Image Ana- lysis and Recognition,” Proceedings of the Second International Conference on Computer Vision Theory and Applications, Barcelona, 8-11 March 2007, pp. 223-229.
 P. Adibi and R. Safabakhsh, “Joint Entropy Maximization in the Kernel-Based Linear Manifold Topographic Map,” Proceedings of International Joint Conference on Neural Networks, Orlando, 12-17 August 2007, pp. 1133- 1138. doi:10.1109/IJCNN.2007.4371117
 K. Shi and Z. Song-Chun, “Mapping Natural Image Patches by Explicit and Implicit Manifolds,” IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, 17-22 June 2007, pp. 1-7.
 C. Pavlopoulou and S. X. Yu, “Indoor-Outdoor Classification with Human Accuracies: Image or Edge Gist?” IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, San Francisco, 13- 18 June 2010, pp. 41-47.
 C. Guo and L. Zhang, “A Novel Multiresolution Spatiotemporal Saliency Detection Model and Its Applications in Image and Video Compression,” IEEE Transactions on Image Processing, Vol. 9, No. 1, 2010, pp. 185-198.
 M. L. Gong and Y. H. Yang, “Quadtree-Based Genetic Algorithm and Its Applications to Computer Vision,” Pattern Recognition, Vol. 37, No. 8, 2004, pp. 1723-1733.
 F. Porikli, L. Davis, et al., “A Comprehensive Evaluation Framework and a Comparative Study for Human Detectors,” IEEE Transactions on Intelligent Transportation Systems, Vol. 10, No. 3, 2009, pp. 417-427.
 A. Holub and P. Perona, “A Discriminative Framework for Modelling Object Classes,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, 20-26 June 2005, pp. 664-671.
 M. Everingham, L. Van Gool, et al., “The Pascal Visual Object Classes (VOC) Challenge,” International Journal of Computer Vision, Vol. 88, No. 2, 2010, pp. 303-338.