Visual Distractors Detecting in Images Using Weighted Two Phase Test Sample Sparse Representation Method
Subject Areas : electrical and computer engineeringF. Sabouri 1 , F. yaghmaee 2 *
1 -
2 - ُSemnan University -Electrical & Computer Engineering Department
Keywords: Human vision systemvisual attentionsalient regionsvisual distractorsparse coding and representation,
Abstract :
The image observer usually wants to receive the message and the main subject of the image in the shortest time. Hence, assuming there is useful information in the salient regions, the human vision system unconsciously guides visual attention towards them. This assumption is not always correct in practice, and in some cases, salient regions merely cause visual distractions. Therefore, in different applications, a mechanism is needed to identify these regions. To prevent from distracting observer’s attention from the main subject, these regions are eliminated. Furthermore, neglecting these regions could be of considerable assistance to the methods that function base on salient regions recognition. So, in this paper, Based on the methods of the class imbalance challenge each segment of training images in the dataset is a partition to 9 classes according to the relevant mask in the dataset, that the number of each class is proportional to its disturbance intensity. Then, segment-based features are extracted and determining the class of each segment is determined according to WTPTSSR method, which is based on the Sparse Coding and Representation system.Finally, in order to precisely analyzing the proposed method and comparing it to other approaches, four analysis criteria with different performances are presented. According to results, despite being time-consuming, the proposed method has a higher accuracy than the previous ones.
[1] N. Senthilkumaran and J. Thimmiaraja, "Histogram equalization for image enhancement using MRI brain images," inProc. World Congress on Computing and Communication Technologies WCCCT'14, pp. 80-83, Trichirappalli, India, 27 Feb.- 1 Mar. 2014.
[2] V. A. Mateescu and I. V. Bajic, "Visual attention retargeting," IEEE MultiMedia, vol. 23, no. 1, pp. 82-91, Jan. 2016.
[3] T. V. Nguyen, et al., "Image re-attentionizing," IEEE Trans. on Multimedia, vol. 15, no. 8, pp. 1910-1919, Dec. 2013.
[4] R. Mechrez, E. Shechtman, and L. Zelnik-Manor, "Saliency driven image manipulation," in Proc. Conf. on Applications of Computer Vision, WACV'18, pp. 1368-1376, Lake Tahoe, NV, USA, 12-15 Mar. 2018.
[5] A. Borji and L. Itti, "State-of-the-art in visual attention modeling," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 35, no. 1, pp. 185-207, Jan. 2013.
[6] F. Sabouri and F. Yaghmaee, "Visual distractors detecting in images using TPTSSR," in Proc. 10th Iranian Conf. on Machine Vision and Image Processing, MVIP'17, pp. 87-92, Isfahan, Iran, 22-23 Nov. 2017.
[7] Y. Xu, D. Zhang, J. Yang, and J. Y. Yang, "A two-phase test sample sparse representation method for use with face recognition," IEEE Trans. on Circuits and Systems for Video Technology, vol. 21, no. 9, pp. 1255-1262, Sept. 2011.
[8] L. Itti, C. Koch, and E. Niebur, "A model of saliency-based visual attention for rapid scene analysis," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 20, no. 11, pp. 1254-1259, Nov. 1998.
[9] A. Oliva and A. Torralba, "Modeling the shape of the scene: a holistic representation of the spatial envelope," International J. of Computer Vision, vol. 42, no. 3, pp. 145-175, May-Jun. 2001.
[10] S. Goferman, L. Zelnik-Manor, and A. Tal, "Context-aware saliency detection," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 34, no. 10, pp. 1915-1926, Oct. 2012.
[11] A. Borji and L. Itti, "Exploiting local and global patch rarities for saliency detection," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, CVPR'12, pp. 478-485, Providence, RI, USA, 16-21 Jun. 2012.
[12] R. Margolin, A. Tal, and L. Zelnik-Manor, "What makes a patch distinct?," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1139-1146, Portland, OR, USA, 23-28 Jun. 2013.
[13] Q. Fan and C. Qi, "Saliency detection based on global and local shortterm sparse representation," Neurocomputing, vol. 175, no. ???, pp. 81-89, Oct. 2016.
[14] N. Dhavale and L. Itti, "Saliency-based multifoveated MPEG compression," in Proc. 7th Int. Symp. on Signal Processing and Its Applications, vol. 1, pp. 229-232, Paris, France, 4-4 Jul. 2003.
[15] C. Guo and L. Zhang, "A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression," IEEE Trans. on Image Processing, vol. 19, no. 1, pp. 185-198, Jan. 2010.
[16] G. Evangelopoulos, K. Rapantzikos, A. Potamianos, P. Maragos, A. Zlatintsi, and Y. Avrithis, "Movie summarization based on audiovisual saliency detection," in Proc. 15th IEEE Int. Conf. on Image Processing, ICIP'08, pp. 2528-2531, San Diego, CA, USA, 12-15 Oct. 2008.
[17] D. Simakov, Y. Caspi, E. Shechtman, and M. Irani, "Summarizing visual data using bidirectional similarity," Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, CVPR'08, 8 pp., Anchorage, AK, USA, 23-28 Jun. 2008.
[18] L. Zhang, Y. Shen, and H. Li, "VSI: a visual saliency-induced index for perceptual image quality assessment," IEEE Trans. on Image Processing, vol. 23, no. 10, pp. 4270-4281, Oct. 2014.
[19] R. Achanta and S. Susstrunk, "Saliency detection for content-aware image resizing," in Proc. 16th IEEE Int. Conf. on Image Processing, ICIP'09, pp. 1005-1008, Cairo, Egypt, 7-10 Nov. 2009.
[20] Y. Fang, et al., "Saliency-based stereoscopic image retargeting," Information Sciences, vol. 372, pp. 347-358, 1 Dec. 2016.
[21] O. Fried, E. Shechtman, D. B. Goldman, and A. Finkelstein, "Finding distractors in images," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, CVPR'15, pp. 1703-1712, Boston, MA, USA, 7-12 Jun. 2015.
[22] R. Tibshirani, "Regression shrinkage and selection via the lasso," J. of the Royal Statistical Society, Series B (Methodological), vol. 58, no. 1, pp. 267-288, Jan. 1996.
[23] J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, "Robust face recognition via sparse representation," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210-227, Feb. 2009.
[24] Z. Zhang, Y. Xu, J. Yang, X. Li, and D. Zhang, "A survey of sparse representation: algorithms and applications," IEEE Access, vol. 3, pp. 490-530, May 2015.
[25] K. Yu, T. Zhang, and Y. Gong, "Nonlinear learning using local coordinate coding," in Proc. of the 22nd International Conference on Neural Information Processing Systems, NIPS’09, pp. 2223-2231, Vancouver, BC, Canada, 7-10 Dec.. 2009.
[26] Z. Liu, J. Pu, M. Xu, and Y. Qiu, "Face recognition via weighted two phase test sample sparse representation," Neural Processing Letters, vol. 41, no. 1, pp. 43-53, Jan. 2015.
[27] H. Zou and T. Hastie, "Regularization and variable selection via the elastic net," J. of the Royal Statistical Society, Series B (Statistical Methodology), vol. 67, no. 2, pp. 301-320, Aug. 2005.
[28] T. Judd, K. Ehinger, F. Durand, and A. Torralba, "Learning to predict where humans look," in Proc. IEEE 12th Int. Conf. on Computer Vision, pp. 2106-2113, Kyoto, Japan, 29 Sept.- 2 Oct. 2009.
[29] E. Rosten, R. Porter, and T. Drummond, "Faster and better: a machine learning approach to corner detection," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 32, no. 1, pp. 105-119, Jan. 2010.
[30] C. L. Zitnick and P. Dollar, "Edge boxes: locating object proposals from edges," in Proc. European Conf. on Computer Vision, ECCV'14, pp. 391-405, Zurich, Switzerland, 6-12 Sept. 2014.
[31] M. M. Cheng, N. J. Mitra, X. Huang, P. H. Torr, and S. M. Hu, "Global contrast based salient region detection," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 37, no. 3, pp. 569-582, Mar. 2015.
[32] M. Wang, J. Konrad, P. Ishwar, K. Jing, and H. Rowley, "Image saliency: from intrinsic to extrinsic context," in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, CVPR'11, pp. 417-424, Colorado Springs, CO, USA, 20-25 Jun. 2011.
[33] K. K. Maninis, J. Pont-Tuset, P. Arbelaez, and L. Van Gool, "Convolutional oriented boundaries: from image segmentation to high-level tasks," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 40, no. 4, pp. 819 - 833, Apr. 2018..
[34] Y. Sun, A. K. Wong, and M. S. Kamel, "Classification of imbalanced data: a review," International J. of Pattern Recognition and Artificial Intelligence, vol. 23, no. 4, pp. 687-719, Nov. 2009.
[35] X. Li, et al., "DeepSaliency: multi-task deep neural network model for salient object detection," IEEE Trans. on Image Processing, vol. 25, no. 8, pp. 3919-3930, Oct. 2016.