A Review of Attention Models in Image Protrusion and Object Detection
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Authors
Seyyed Mohammad Reza Hashemi
- Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Ali Broumandnia
- Faculty of Computer and Information Technology Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
Abstract
Modelling in visual attention especially the stimulus-driven one, i.e. saliency-based attention, has been a very active research field during the recent 25 years. There are many attention models which, apart from being in other aspects, have been offered in successful functions of computer vision, moving robots, and cognitive systems. The present article surveys the primary concepts of visual attention, implemented in cognitive, Bayesian network, decision theories, and information theory in a computational perspective. It will demonstrate a categorization that provides a critical comparison of the approaches as well as their abilities and results. Specifically, the article formulates the criteria, derived from computational behaviors and studies in order to compare the quality of visual attention models.
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ISRP Style
Seyyed Mohammad Reza Hashemi, Ali Broumandnia, A Review of Attention Models in Image Protrusion and Object Detection, Journal of Mathematics and Computer Science, 15 (2015), no. 4, 261-271
AMA Style
Hashemi Seyyed Mohammad Reza, Broumandnia Ali, A Review of Attention Models in Image Protrusion and Object Detection. J Math Comput SCI-JM. (2015); 15(4):261-271
Chicago/Turabian Style
Hashemi, Seyyed Mohammad Reza, Broumandnia, Ali. "A Review of Attention Models in Image Protrusion and Object Detection." Journal of Mathematics and Computer Science, 15, no. 4 (2015): 261-271
Keywords
- visual attention
- bottom-up attention
- top-down attention
- saliency
- eye movement
- regions of interest
- visual search.
MSC
References
-
[1]
A. Borji, State-of-the-Art in Visual Attention Modeling, IEEE Transactions on Pattern Analysis And Machine Intelligence, Vol. 35 , No. 1, January. (2013)
-
[2]
L. Itti, Models of Bottom-Up and Top-Down Visual Attention, PhD thesis, California Inst. of Technology (2000)
-
[3]
L. Itti, C. Koch, E. Niebur, A Model of Saliency-Based Visual Attention for Rapid Scene Analysis, IEEE Trans. Pattern Analysis and Machine Intelligence, 20 (1998), 1254-1259
-
[4]
Y. Zhai, M. Shah, Visual Attention Detection in Video Sequences Using Spatiotemporal Cues, Proc. ACM Int’l Conf., Multimedia (2006)
-
[5]
L. Zhang, M. H. Tong, T. K. Marks, H. Shan, G. W. Cottrell, SUN: A Bayesian Framework for Saliency Using Natural Statistics, J. Vision, 8 (2008), 1-20.
-
[6]
L. Itti, Quantifying the Contribution of Low-Level Saliency to Human Eye Movements in Dynamic Scenes, Visual Cognition, 12 (2005), 1093-1123.
-
[7]
R. Rao, Bayesian Inference and Attentional Modulation in the Visual Cortex, NeuroReport, 16 (2005), 1843-1848.
-
[8]
Y.-S. Wang, C.-L. Tai, O. Sorkine, T.-Y. Lee, Optimized scale-and-stretch for image resizing, ACM Trans. on Graphics (Proc. of SIGGRAPH ASIA) 27(5), (2008)
-
[9]
M. Ma, J. K. Guo, Automatic image cropping for mobile device with built-in camera, in [IEEE Consumer Communications and Networking Conference], (2004), 710 – 711
-
[10]
Y. Guo, F. Liu, J. Shi, Z.-H. Zhou, M. Gleicher, Image retargeting using mesh parametrization, IEEE Trans. on Multimedia, 11(5) (2009), 856–867
-
[11]
SMR. Hashemi, M. Kalantari, M. Zangian, Giving a New Method for Face Recognition Using Neural Networks, International Journal of Mechatronics, Electrical and Computer Technology, 4(11) (2014), 744-761
-
[12]
SMR. Hashemi, M. Zangian, M. Shakeri, M. Faridpoor, Survey Article about Image Fuzzy Processing Algorithms, The Journal of Mathematics and Computer Science, 13 (2014), 26-40.
-
[13]
SMR. Hashemi, Review of algorithms changing image size, Cumhuriyet Science Journal, Vol. 36 , (2015)