Abstract
Image retargeting is the task of making images capable of being displayed on screens with different sizes. This work should be done so that high-level visual information and low-level features such as texture remain as intact as possible to the human visual system. At the same time, the output image may have different dimensions. Thus, simple methods such as scaling and cropping are not adequate for this purpose. In recent years, researchers have tried to improve the existing retargeting methods, and they have introduced new ones. However, a specific method cannot be utilized to retarget all types of images. In other words, different images require different retargeting methods. Image retargeting has a close relationship to image saliency detection, which is a relatively new image processing task. Earlier saliency detection methods were based on local and global but low-level image information. These methods are called bottom-up processes. On the other hand, newer approaches are top-down and mixed methods that consider the high level and semantic knowledge of the image too. In this paper, we introduce the proposed methods in both saliency detection and retargeting. For the saliency detection, the use of image context and semantic segmentation are examined, and a novel mixed bottom-up and top-down saliency detection method is introduced. After saliency detection, a modified version of an existing retargeting technique is utilized for retargeting the images. The results suggest that the proposed image retargeting pipeline has excellent performance compared to other tested methods. Also, the subjective evaluations on the Pascal dataset can be used as a retargeting quality assessment dataset for further research.
Similar content being viewed by others
References
Avidan S, Shamir A (2007) Seam carving for content-aware image resizing. In: ACM Transactions on Graphics (TOG), vol. 26, p. 10. ACM
Borji A, Itti L (2015) Cat2000: A large scale fixation dataset for boosting saliency research. arXiv preprint arXiv:1505.03581
Cho D, Park J, Oh TH, Tai YW, So Kweon I (2017) Weakly-and self-supervised learning for content-aware deep image retargeting. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4558–4567
Cornia M, Baraldi L, Serra G, Cucchiara R (2018) Predicting human eye fixations via an lstm-based saliency attentive model. IEEE Trans Image Process 27(10):5142–5154
Esmaeili SA, Singh B, Davis LS (2017) Fast-at: Fast automatic thumbnail generation using deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4622–4630
Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A (2012) The PASCAL Visual Object Classes Challenge (VOC2012) Results. http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html
Fang Y, Fang Z, Yuan F, Yang Y, Yang S, Xiong NN (2017) Optimized multioperator image retargeting based on perceptual similarity measure. IEEE Transactions on Systems, Man, and Cybernetics: Systems 47(11):2956–2966
Fujimura M, Imamura K, Kuroda H (2017) Application of saliency map to restraint scheme of attack to digital watermark using seam carving. In: 2017 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), pp. 347–348. IEEE
Goferman S, Zelnik-Manor L, Tal A (2011) Context-aware saliency detection. IEEE Trans Pattern Anal Mach Intell 34(10):1915–1926
Guo C, Zhang L (2010) A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans Image Process 19(1):185–198
Hooshmand M, Soroushmehr SMR, Khadivi P, Samavi S, Shirani S (2013) Visual sensor network lifetime maximization by prioritized scheduling of nodes. J Netw Comput Appl 36(1):409–419
Hu W, Hu R, Xie N, Ling H, Maybank S (2014) Image classification using multiscale information fusion based on saliency driven nonlinear diffusion filtering. IEEE Trans Image Process 23(4):1513–1526
Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis & Machine Intelligence 11:1254–1259
Kim J, Han D, Tai YW, Kim J (2016) Salient region detection via high-dimensional color transform and local spatial support. IEEE Trans Image Process 25(1):9–23
Kruthiventi SS, Ayush K, Babu RV (2017) Deepfix: a fully convolutional neural network for predicting human eye fixations. IEEE Trans Image Process 26(9):4446–4456
Li C, Wand M (2015) Approximate translational building blocks for image decomposition and synthesis. ACM Transactions on Graphics (TOG) 34(5), 158
Li H, Chen J, Lu H, Chi Z (2017) Cnn for saliency detection with low-level feature integration. Neurocomputing 226:212–220
Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft coco: Common objects in context. In: European conference on computer vision, pp. 740–755. Springer
Ma L, Lin W, Deng C, Ngan KN (2012) Image retargeting quality assessment: a study of subjective scores and objective metrics. IEEE Journal of Selected Topics in Signal Processing 6(6):626–639
Panozzo D,Weber O, Sorkine O (2012) Robust image retargeting via axis-aligned deformation. In: Computer Graphics Forum, vol. 31, pp. 229–236. Wiley Online Library
Pritch Y, Kav-Venaki E, Peleg S (2009) Shift-map image editing. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 151–158. IEEE
Rabbani N, Nazari B, Sadri S, Rikhtehgaran R (2017) Efficient Bayesian approach to saliency detection based on dirichlet process mixture. IET Image Process 11(11):1103–1113
Razzaghi P, Samavi S (2015) Image retargeting using nonparametric semantic segmentation. Multimed Tools Appl 74(24):11517–11536
Rubinstein M, Shamir A, Avidan S (2008) Improved seam carving for video retargeting. In: ACM transactions on graphics (TOG), vol. 27, p. 16. ACM
Rubinstein M, Shamir A, Avidan S (2009) Multi-operator media retargeting. ACM Transactions on graphics (TOG) 28(3), 23
Rubinstein M, Gutierrez D, Sorkine O, Shamir A (2010) A comparative study of image retargeting. In: ACM transactions on graphics (TOG), vol. 29, p. 160. ACM
Shafieyan F, Karimi N, Mirmahboub B, Samavi S, Shirani S (2017) Image retargeting using depth assisted saliency map. Signal Process Image Commun 50:34–43
Shao F, Lin W, Lin W, Jiang Q, Jiang G (2017) QoE-guided warping for stereoscopic image retargeting. IEEE Trans Image Process 26(10):4790–4805
Simakov D, Caspi Y, Shechtman E, Irani M (2008) Summarizing visual data using bidirectional similarity. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
Tang F, Dong W, Meng Y, Ma C, Wu F, Li X, Lee TY (2018) Image retargetability. arXiv preprint arXiv:1802.04392
Tian H, Fang Y, Zhao Y, Lin W, Ni R, Zhu Z (2014) Salient region detection by fusing bottom-up and top-down features extracted from a single image. IEEE Trans Image Process 23(10):4389–4398
Wang L, Chen C (2016) Study on the image retargeting by using semantic concepts. International Journal of Signal Processing, Image Processing and Pattern Recognition 9(3):281–290
Wang Q, Yuan Y (2014) High quality image resizing. Neurocomputing 131:348–356
Wang YS, Tai CL, Sorkine O, Lee TY (2008) Optimized scale-and-stretch for image resizing. In: ACM Transactions on Graphics (TOG), vol. 27, p. 118. ACM
Wang Q, Yuan Y, Yan P, Li X (2013) Saliency detection by multiple-instance learning. IEEE transactions on cybernetics 43(2):660–672
Wang K, Lin L, Lu J, Li C, Shi K (2015) Pisa: Pixelwise image saliency by aggregating complementary appearance contrast measures with edge-preserving coherence. IEEE Trans Image Process 24(10):3019–3033
Wang W, Shen J, Yang R, Porikli F (2018) Saliency-aware video object segmentation. IEEE Trans Pattern Anal Mach Intell 40(1):20–33
Wolf L, Guttmann M, Cohen-Or D (2007) Non-homogeneous content-driven video-retargeting. In: 2007 IEEE 11th International Conference on Computer Vision, pp. 1–6. IEEE
Yan Q, Xu L, Shi J, Jia J (2013) Hierarchical saliency detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1155–1162
Yan B, Li K, Yang X, Hu T (2015) Seam searching-based pixel fusion for image retargeting. IEEE Transactions on Circuits and Systems for Video Technology 25(1):15–23
Zhao H. Pyramid scene parsing network. https://github.com/hszhao/PSPNet/
Zhao R, Ouyang W, Li H, Wang X (2015) Saliency detection by multi-context deep learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1265–1274
Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2881–2890
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ahmadi, M., Karimi, N. & Samavi, S. Context-aware saliency detection for image retargeting using convolutional neural networks. Multimed Tools Appl 80, 11917–11941 (2021). https://doi.org/10.1007/s11042-020-10185-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-10185-0