Automatic browsing of large pictures on mobile devices

Pictures have become increasingly common and popular in mobile communications. However, due to the limitation of mobile devices, there is a need to develop new technologies to facilitate the browsing of large pictures on the small screen. In this paper, we propose a novel approach which is able to automate the scrolling and navigation of a large picture with a minimal amount of user interaction on mobile devices. An image attention model is employed to illustrate the information structure within an image. An optimal image browsing path is then calculated based on the image attention model to simulate the human browsing behaviors. Experimental evaluations of the proposed mechanism indicate that our approach is an effective way for viewing large images on small displays.

[1]  Thierry Pun,et al.  Attentive mechanisms for dynamic and static scene analysis , 1995 .

[2]  Lingyu Chen,et al.  Identification of fixations in reading eye movements by a multi-layer neural network , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[3]  John R. Smith,et al.  Content-based transcoding of images in the Internet , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[4]  John R. Smith,et al.  Adapting Multimedia Internet Content for Universal Access , 1999, IEEE Trans. Multim..

[5]  Allan Kuchinsky,et al.  Framework for adaptive content delivery in heterogeneous network environments , 1999, Electronic Imaging.

[6]  E. Chang.  Wavelet Foveation , 1999 .

[7]  Christof Koch,et al.  Comparison of feature combination strategies for saliency-based visual attention systems , 1999, Electronic Imaging.

[8]  Touradj Ebrahimi,et al.  Christopoulos: Thc Jpeg2000 Still Image Coding System: an Overview the Jpeg2000 Still Image Coding System: an Overview , 2022 .

[9]  Oscar de Bruijn,et al.  Rapid serial visual presentation: a space-time trade-off in information presentation , 2000, AVI '00.

[10]  C. Koch,et al.  Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.

[11]  HongJiang Zhang,et al.  Text Area Detection from Video Frames , 2001, IEEE Pacific Rim Conference on Multimedia.

[12]  Lawrence W. Stark,et al.  Top-down guided eye movements , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[13]  Ed H. Chi,et al.  Using information scent to model user information needs and actions and the Web , 2001, CHI.

[14]  J. O'Regan,et al.  Fixation location effects on fixation durations during reading: an inverted optimal viewing position effect , 2001, Vision Research.

[15]  Jiebo Luo,et al.  Displaying images on mobile devices: capabilities, issues, and solutions , 2002, Proceedings. International Conference on Image Processing.

[16]  Harry Shum,et al.  Statistical Learning of Multi-view Face Detection , 2002, ECCV.

[17]  Xing Xie,et al.  Looking into video frames on small displays , 2003, ACM Multimedia.

[18]  HongJiang Zhang,et al.  Contrast-based image attention analysis by using fuzzy growing , 2003, MULTIMEDIA '03.

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

[20]  Xing Xie,et al.  Visual attention based image browsing on mobile devices , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).