@INPROCEEDINGS{Kiess_2012a,
author={Kiess, J. and Garcia, R. and Kopf, S. and Effelsberg, W.},
booktitle={Multimedia and Expo Workshops (ICMEW), 2012 IEEE International Conference on},
title={Improved Image Retargeting by Distinguishing between Faces in Focus and Out of Focus},
year={2012},
month={July},
pages={145-150},
abstract={The identification of relevant objects in an image is highly relevant in the context of image retargeting. Especially faces draw the attention of viewers. But the level of relevance may change between different faces depending on the size, the location, or whether a face is in focus or not. In this paper, we present a novel algorithm which distinguishes in-focus and out-of-focus faces. A face detector with multiple cascades is used first to locate initial face regions. We analyze the ratio of strong edges in each face region to classify out-of-focus faces. Finally, we use the Grab Cut algorithm to segment the faces and define binary face masks. These masks can then be used as an additional input to image retargeting algorithms.},
keywords={edge detection;face recognition;image classification;image segmentation;object detection;GrabCut algorithm;binary face masks;face detector;face regions;face segmentation;image retargeting algorithms;in-focus faces;object identification;out-of-focus face classification;strong-edge ratio;Classification algorithms;Clustering algorithms;Context;Face detection;Image edge detection;Image resolution;Image segmentation;face detection;focus detection;grabcut;image resizing;image retargeting;saliency of faces;seam carving},
doi={10.1109/ICMEW.2012.32},}