Optimized image resizing using seam carving and scaling

We present a novel method for content-aware image resizing based on optimization of a well-defined image distance function, which preserves both the important regions and the global visual effect (the background or other decorative objects) of an image. The method operates by joint use of seam carving and image scaling. The principle behind our method is the use of a bidirectional similarity function of image Euclidean distance (IMED), while cooperating with a dominant color descriptor (DCD) similarity and seam energy variation. The function is suitable for the quantitative evaluation of the resizing result and the determination of the best seam carving number. Different from the previous simplex-mode approaches, our method takes the advantages of both discrete and continuous methods. The technique is useful in image resizing for both reduction/retargeting and enlarging. We also show that this approach can be extended to indirect image resizing.

[1]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[2]  Ariel Shamir,et al.  Cropping Scaling Seam carving Warping Multi-operator , 2009 .

[3]  John Hart,et al.  ACM Transactions on Graphics , 2004, SIGGRAPH 2004.

[4]  Olga Sorkine-Hornung,et al.  Optimized scale-and-stretch for image resizing , 2008, SIGGRAPH Asia '08.

[5]  Ariel Shamir,et al.  Improved seam carving for video retargeting , 2008, ACM Trans. Graph..

[6]  Xing Xie,et al.  Automatic browsing of large pictures on mobile devices , 2003, MULTIMEDIA '03.

[7]  Denis Simakov,et al.  Summarizing visual data using bidirectional similarity , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Larry S. Davis,et al.  Multi-scale video cropping , 2007, ACM Multimedia.

[9]  Jan A Snyman,et al.  Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms , 2005 .

[10]  Jing Li,et al.  An adaptive image Euclidean distance , 2009, Pattern Recognit..

[11]  Yan Zhang,et al.  On the Euclidean distance of images , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Christof Koch,et al.  Modeling attention to salient proto-objects , 2006, Neural Networks.

[13]  Daniel Cohen-Or,et al.  Non-homogeneous Content-driven Video-retargeting , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[14]  Xing Xie,et al.  A visual attention model for adapting images on small displays , 2003, Multimedia Systems.

[15]  William T. Freeman,et al.  The patch transform and its applications to image editing , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Heng-Da Cheng,et al.  Effective image retrieval using dominant color descriptor and fuzzy support vector machine , 2009, Pattern Recognit..

[17]  B. S. Manjunath,et al.  Color and texture descriptors , 2001, IEEE Trans. Circuits Syst. Video Technol..

[18]  Yael Pritch,et al.  Shift-map image editing , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[19]  Kun Zhou,et al.  Inverse texture synthesis , 2008, ACM Trans. Graph..

[20]  Benjamin B. Bederson,et al.  Automatic thumbnail cropping and its effectiveness , 2003, UIST '03.

[21]  David Salesin,et al.  Gaze-based interaction for semi-automatic photo cropping , 2006, CHI.

[22]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[23]  KochChristof,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 1998 .

[24]  Daniel Cohen-Or,et al.  Feature-aware texturing , 2006, EGSR '06.

[25]  Douglas DeCarlo,et al.  Stylization and abstraction of photographs , 2002, ACM Trans. Graph..

[26]  Weiming Dong,et al.  Optimized image resizing using seam carving and scaling , 2009, SIGGRAPH 2009.

[27]  Ralph R. Martin,et al.  Shrinkability Maps for Content‐Aware Video Resizing , 2008, Comput. Graph. Forum.