Focusness Extraction from Images for Automatic Camera Control by Measuring Objectness

Current digital cameras have various automatic control systems. In automatic camera systems, extracting focusness from an image is a very important problem. Automatic extraction of the main subject makes taking photos very easy, even for an amateur photographer. Methods have been proposed to evaluate focusness by visual saliency, which assume that an area with high saliency also has high focusness. However, various differences exist between focusness and saliency. In this study, we compare the values between focusness and saliency maps. We evaluate the focusness of 80 images in an image evaluation experiment with 20 observers. Saliency maps are calculated using six conventional algorithms. We show that the individual variations of focusness are very few in images that include only one major object. Furthermore, we apply a GIST feature to the saliency method by using a center-surround histogram and extract focusness from images with high accuracy.

[1]  Laurent Itti,et al.  Mobile robot vision navigation & localization using Gist and Saliency , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  Aswin C. Sankaranarayanan,et al.  Compressive epsilon photography for post-capture control in digital imaging , 2014, ACM Trans. Graph..

[3]  P. Hanrahan,et al.  Light Field Photography with a Hand-held Plenoptic Camera , 2005 .

[4]  吉尔·伊斯雷尔·多贡,et al.  Image processor and methods for processing an image , 2016 .

[5]  Liqing Zhang,et al.  Saliency Detection: A Spectral Residual Approach , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[7]  A. Akobeng,et al.  Understanding diagnostic tests 3: receiver operating characteristic curves , 2007, Acta paediatrica.

[8]  G. Underwood,et al.  Congruency, saliency and gist in the inspection of objects in natural scenes , 2007 .

[9]  Yoichi Sato,et al.  Sensation-based photo cropping , 2009, ACM Multimedia.

[10]  William T. Freeman,et al.  Presented at: 2nd Annual IEEE International Conference on Image , 1995 .

[11]  Antonio Torralba,et al.  Top-down control of visual attention in object detection , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[12]  Jiebo Luo,et al.  Subject Content-Based Intelligent Cropping of Digital Photos , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[13]  Shree K. Nayar,et al.  Transactions on Pattern Analysis and Machine Intelligence Flexible Depth of Field Photography 1 Depth of Field , 2022 .

[14]  S. Süsstrunk,et al.  Frequency-tuned salient region detection , 2009, CVPR 2009.

[15]  Carlos R. del-Blanco,et al.  Learning-based depth estimation from 2D images using GIST and saliency , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[16]  Laurent Itti,et al.  Saliency and Gist Features for Target Detection in Satellite Images , 2011, IEEE Transactions on Image Processing.

[17]  Pietro Perona,et al.  Graph-Based Visual Saliency , 2006, NIPS.

[18]  Antonio Torralba,et al.  Depth Estimation from Image Structure , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Nanning Zheng,et al.  Learning to Detect a Salient Object , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[21]  Frédo Durand,et al.  A Benchmark of Computational Models of Saliency to Predict Human Fixations , 2012 .