CRIST900: A Fully-Labeled Natural Image Dataset for Multi-Operator Content Aware Image Retargeting

  • M. AbhayadevEmail author
  • T. Santha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 940)


A fully-labeled image dataset provides an exclusive resource for reproducible analysis, investigation inquiries and data analyses in different research computational fields like machine learning, computer vision and deep learning machine intelligence. This research paper present a large scale fully-labeled natural image dataset for Multi-Operator content aware image retargeting techniques. The image dataset is feely available for image processing research field. The current research natural image dataset entitled CRIST900, it include 900 natural images and uses for content aware image retargeting. The proposed CRIST is an image retargeting Multi-Operator method called Content Retargeting Image reSizing Technique. The proposed image resizing method has three phases, image object boundary identification, the image objects feature importance visual saliency map generation and Multi-Operator techniques for efficient retargeting of image objects. The Multi-Operator retargeted image quality assessment was done by different subjective and objective image quality assessment matrices. The experimental result shows that the proposed approach can attain better result than the traditional methods and content aware state-of-the-art image retargeting techniques. The research dataset is publicly available at


Seam carving Multi-Operator Saliency map Scaling Retargeting 



We express our respect and gratitude for the great help and contributions of Mr. Aneesh T K (Senior Video Editor Amrita Television, Calicut Bureau, Kerala), Mr. Arun Krishnan P (Field Sales Officer in Hegde & Hegde Pharmaceutica LLP, Calicut, Kerala) and Mr. Sakthi Shiva Kumar (Agricultural Scientist and Researcher, Tamilnadu) in collecting the natural image dataset CRIST900 and for giving many valuable suggestions in completing this research.


  1. Hwang, D.-S., Chien, S.-Y.: Content-aware image resizing using perceptual seam carving with human attention model. In: IEEE International Conference on Multimedia and Expo, pp. 1029–1032 (2008)Google Scholar
  2. Han, J.-W., Choi, K.-S., Wang, T.-S., Cheon, S.-H., Ko, S.-J.: Improved seam carving using a modified energy function based on wavelet decomposition. In: IEEE 13th International Symposium on Consumer Electronics ISCE, pp. 38–41 (2009)Google Scholar
  3. Abhayadev, M., Santha, T.: Object boundary identification using enhanced high pass frequency filtering algorithm and morphological erosion structuring element. J. Sci. Ind. Res. (SCIE) 76, 620–625 (2017)Google Scholar
  4. Abhayadev, M., Santha, T.: Efficient retargeting of shadow images using improved CRIST. In: IEEE International Conference on Intelligent Computing and Control (I2C2), pp. 1–5 (2017b)Google Scholar
  5. Abhayadev, M., Santha, T.: Content-aware image seam carving technique for object resizing. Saba J. Inf. Technol. Netw. (SJITN) 5(1) (2017c). ISSN 2312-4989Google Scholar
  6. Abhayadev, M., Santha, T.: Image retargeting based on object inpainting and background seam carving. In: IEEE International Conference on Advances in Electrical Technology for Green Energy (ICAETGT) (2017d)Google Scholar
  7. Rubinstein, M., Shamir, A., Avidan, S.: Multi-operator media retargeting. ACM Trans. Graph. (TOG) 28(3), 23 (2009)CrossRefGoogle Scholar
  8. Wei-Ming, D., Bao, G.-B., Zhang, X.-P., Paul, J.-C.: Fast multi-operator image resizing and evaluation. J. Comput. Sci. Technol. 27(1), 121–134 (2012)CrossRefGoogle Scholar
  9. Shi, M., Peng, G., Yang, L., Xu, D.: A content-aware image resizing method with prominent object size adjusted. In: Proceedings of the 17th ACM Symposium on Virtual Reality Software and Technology, pp. 175–176 (2010b)Google Scholar
  10. Luo, S., Zhang, J., Zhang, Q., Yuan, X.: Multi-operator image retargeting with automatic integration of direct and indirect seam carving. Image Vis. Comput. 30(9), 655–667 (2012)CrossRefGoogle Scholar
  11. Wang, Y.-S., Tai, C.-L., Olga, S., Lee, T.-Y.: Optimized scale-and-stretch for image resizing. ACM Trans. Graph. (TOG) 27(5), 118 (2008)CrossRefGoogle Scholar
  12. Niu, Y., Liu, F., Li, X., Gleicher, M.: Image resizing via nonhomogeneous warping. Multimed. Tools Appl. 56(3), 485–508 (2012)CrossRefGoogle Scholar
  13. Shai, A., Shamir, A.: Seam carving for content-aware image resizing. ACM Trans. Graph. (TOG) 26(3), 10 (2007)CrossRefGoogle Scholar
  14. Pritch, Y., Eitam, K.-V., Peleg, S.: Shift-map image editing. In: IEEE 12th International Conference on Computer Vision, pp. 151–158 (2009)Google Scholar
  15. Zachi, K., Freedman, D., Gotsman, C.: Energy-based image deformation. Comput. Graph. Forum 28(5), 1257–1268 (2009)CrossRefGoogle Scholar
  16. Chen, R., Daniel, F., Zachi, K., Craig, G., Liu, L.: Content-aware image resizing by quadratic programming. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1–8 (2010)Google Scholar
  17. Chen, Y.-L., Huang, T.-W., Chang, K.-H., Tsai, Y.-C., Chen, H.-T., Chen, B.-Y.: Quantitative analysis of automatic image cropping algorithms: a dataset and comparative study. In: IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 226–234 (2017)Google Scholar
  18. Kao, Y., He, R., Huang, K.: Automatic image cropping with aesthetic map and gradient energy map. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1982–1986 (2017)Google Scholar
  19. Erkut, A., Aydin, T., Özden, K.E.: Automatic scale and image selection for panoramic images. In: IEEE 24th Conference on Signal Processing and Communication Application (SIU), pp. 2001–2004 (2016)Google Scholar
  20. Feng, S., Lin, W., Lin, W., Jiang, G., Yu, M., Fu, R.: Stereoscopic visual attention guided seam carving for stereoscopic image retargeting. J. Disp. Technol. 12(1), 22–30 (2016)CrossRefGoogle Scholar
  21. Yun, L., Liu, Y.-J., Gutierrez, D.: Objective quality prediction of image retargeting algorithms. IEEE Trans. Visual Comput. Graph. 23(2), 1099–1110 (2017)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceDr G R Damodaran College of ScienceCoimbatoreIndia

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