Contrast description is a very important and commonly used feature description method in the image saliency detection, according to the difference of contrastive region, the detection algorithm can be divided into local and global contrast algorithm. In this paper, firstly, the local contrast AC algorithm is improved, from two aspects of distance calculation method, weighting coefficient and multi-scale average, to improve the problem of emphasizing small and medium-sized areas of significant targets in the original AC algorithm, the experimental results show the effectiveness of the improved scheme. Then the global contrast RC algorithm is analyzed and improved, and the original RC algorithm is improved from two aspects of image pre-segmentation Algorithm improvement and significant center position optimization, the experimental results show the effectiveness of the improved scheme.

Yao Fei. The improved local and global contrast image saliency detection algorithm. Academic Journal of Computing & Information Science (2020), Vol. 3, Issue 5: 8-16. https://doi.org/10.25236/AJCIS.2020.030502.

[1] Wei Yu. Research on image saliency region detection method and its application [D]. Jinan: Shandong University, 2012.

[2] Jing Hui-Jun. Research on key technologies of visual saliency detection [D]. Haerbin: Harbin Institute of Technology, 2014.

[3] Xing Qing, et al. Image saliency detection based on color feature and contrast feature [J]. Semiconductor Optoelectronics, 2019, 40(3): 433-437.

[4] K. Hirata, E. Kasutani and Y. Hara. On image segmentation for object-based image retrieval[C]. Proc. IEEE Int. Conf. Pattern Recognit. 2002:1031-1034.

[5] J. Stottinger, A. Hanbury, N. Sebe and T. Gevers. Sparse Color Interest Points for Image Retrieval and Object Categorization [J]. Image Processing, IEEE Transactions on. 2012,21(5):2681-2693.

[6] Achanta R, Susstrunk S. Saliency detection for content-aware image resizing[C]. Proc. Image Processing(ICIP).2009:1005-1008.

[7] C. Christopoulos, A. Skodras and T. Ebrahimi. The JPEG2000 still image coding system: an overview[J]. IEEE Trans. Consumer Elec. 2002,46(4):1103-1127.

[8] Chen D, Wu C. Object-based multi-feature competitive model for visual saliency detection[C].Proc. the 2nd Internation Conference on Intelligent Systems Design and EngineeringApplications. 2012: 1079-1082.

[9] Ao Huanhuan, Yu Nenghai, Li Weihai.  Image saliency modeling based on region feature[J]. Journal of the University of Science and Technology of China, 2013, 43(10)：837-842.

[10] X. Shen and Y. Wu, A unified approach to salient object detection via low rank matrix recovery[C]. Proc. Computer Vision and Pattern Recognition (CVPR). 2012.

[11] Li Yong.  Research on visual saliency detection algorithm based on region contrast [D]. Shanghai: Shanghai Jiao Tong University, 2013.

[12] E. Rahtu, J. Kannala, M, Salo, J. Heikkila. Segmenting salient objects from images and videos[C]. Proc. European Conference on Computer Vision,2010.

[13] Yang Jun. Research on the computation of visual saliency and its application [D]. South China University of Technology, 2014.

[14] Ma Y F, Zhang H J. Contrast-based image attention analysis by using fuzzy growing[C] //Proceedings of the 11th ACM International Conference on Multimedia. New York: ACM Press,2003: 374-381.

[15] Liu T, Yuan Z, Sun J, et al. learning to detect a salient object[J].IEEE Transaction on Pattern Analysis and Machine Intelligence,2011, 33(2): 353-367.

[16] Perazzi F, Krahenbuhl P, Pritch Y, et al. Saliency filters: contrast based filtering for salient region detection[C] //Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press,2012: 733-740.

[17] Cheng M M, Zhang G X, Mitra N J, et al. Global contrast based salient region detection[C] //Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society Press, 2015: 409-416.

[18] Achanta R, Estrada F, Wils P, et al. Salient region detection and segmentation[C] //Proceedings of the 6th International Conference on Computer Vision Systems. Heidelberg: Springer, 2008:66-75.

[19] Felzenszwalb P F, Huttenlocher D P. Efficient Graph-Based Image Segmentation[J]. International Journal of Computer Vision, 2004, 59(2):167-181.

[20] Radhakrishna Achanta, Appu Shaji, Kevin Smith, et al. SLIC Superpixels[J]. EPFL Technical Report no. 149300, 2010.