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.
展开▼
