Abstract:For the image distortion of over-carved, this paper proposes a modified Seam Carving (SC) method based on image blocking. Images are segmented into protected and non-protected blocks according to the labelled averaged column summation energy vectors, and then each block is allocated the corresponding carving seams. Moreover, the cumulative energy map is optimized in order to reduce the possibility of the small significant regions to be cut off. This paper fused blocking with the SC method, and optimized the cumulative energy map, which can make a carving balance between the object and background parts. In the MSRA database, the proposed algorithm are compared with the SC method and its improved methods. The experimental results are evaluated on the Internet to test their subjective perceptions, which shows that the proposed method has a better subjective perception, and a general applicability for different images.
THÉVENAZ P, BLU T, and UNSER M. Interpolation revisited [medical images application][J]. IEEE Transactions on Medical Imaging, 2000, 19(7): 739-758. doi: 10.1109/42. 875199.
[2]
SURESHA D and PRAKASH H N. Single picture super resolution of natural images using N-Neighbor Adaptive Bilinear Interpolation and absolute asymmetry based wavelet hard thresholding[C]. 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology, Bangalore, India, 2016: 387-393.
XIAO Zhitao, FENG Tiejun, ZHANG Fang, et al. Image interpolation with corner preserving based on partial differential equation[J]. Journal of Electronics & Information Technology, 2015, 37(8): 1892-1899. doi: 10.11999/JEIT 141420.
[4]
CHEN Y L, HUANG T W, CHANG K H, et al. Quantitative analysis of automatic image cropping algorithms: A dataset and comparative study[C]. IEEE Conference on Applications of Computer Vision, Santa Rosa, CA, USA, 2017: 226-234.
[5]
CHEN Jiansheng, BAI Gaocheng, LIANG Shaoheng, et al. Automatic image cropping: A computational complexity study[C]. IEEE Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016: 507-515.
[6]
AVIDAN S and SHAMIR A. Seam carving for content-aware image resizing[J]. ACM Transactions on Graphics, 2007, 26(3): 101-109. doi: 10.1145/1275808.1276390.
NIE Dongdong, MA Qinyong, and MA Lizhuang. Seam carving algorithm based on gradient vector direction analysis [J]. Journal of Electronics & Information Technology, 2012, 34(6): 1506-1510. doi: 10.3724/SP.J.1146.2011.01171.
[8]
RAZ G, SHMUELI R, and KATZ E. Texture segmentation for seam carving[C]. IEEE Conference on Science of Electrical Engineering, Eilat, Israel, 2017: 1-5.
[9]
MANSFIELD A, GEHLER P, VAN GOOL L, et al. Scene carving: Scene consistent image retargeting[C]. European Conference on Computer Vision, Heraklion, Crete, Greece, 2010: 143-156.
[10]
DOMINGUES D, ALAHI A, and VANDERGHEYNST P. Stream carving: An adaptive seam carving algorithm[C]. IEEE International Conference on Image Processing, Hong Kong, China, 2010: 901-904.
[11]
AGHCHEHKOHAL M G and KUMARA W G C W. Improved seam carving using meta-heuristics algorithms combination[C]. IEEE Signal Processing and Intelligent Systems Conference, Tehran, Iran, 2015: 43-47.
[12]
LIN Xiao, SHENG Bin, MA Lizhuang, et al. Seamlet carving for shape-aware image resizing[J]. Science China Information Sciences, 2012, 55(5): 1073-1081. doi: 10.1007/s11432-012- 4565-z.
[13]
ZHOU Bin, WANG Xuanyin, CAO Songxiao, et al. Optimal bi-directional seam carving for compressibility-aware image retargeting[J]. Journal of Visual Communication & Image Representation, 2016, 41: 21-30. doi: 10.1016/j.jvcir.2016.09. 002.
[14]
SHAFIEYAN F, KARIMI N, MIRMAHBOUB B, et al. Image retargeting using depth assisted saliency map[J]. Image Communication, 2016, 50(C): 34-43. doi: 10.1016/j. image.2016.10.006.
ZHAO Danfeng, WANG Bo, and YANG Dawei. Content- aware image based on radom permutation[J]. Journal of Jilin University (Engineering and Technology Edition), 2015, 45(4): 1324-1328. doi: 10.13229/j.cnki. jdxbgxb201504043.
[16]
ZHU Wangjiang, LIANG Shuang, WEI Yichen, et al. Saliency optimization from robust background detection[C]. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014: 2814-2821.