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Learning to Extract Focused Objects From Low DOF Images

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2 Author(s)
Hongliang Li ; School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China ; King N. Ngan

This paper proposes an approach to extract focused objects (i.e., attention objects) from low depth-of-field images. To recognize the focused object, we decompose the image into multiple regions, which are described by using three types of visual descriptors. Each descriptor is extracted from a representation of some aspects of local appearance, e.g., a spatially localized texture, color, or geometrical property. Therefore, the focus detection of a region can be achieved by the classification of extracted visual descriptors based on a binary classifier. We employ a boosting algorithm to learn the classifier with a cascade of decision structure. Given a test image, initial segmentation can be achieved using obtained classification results. Finally, we apply a post-processing technique to improve the results by incorporating region grouping and pixel-level segmentation. Experimental evaluation on a number of images demonstrates the performance advantages of the proposed method, when compared with state-of-the-art methods.

Published in:

IEEE Transactions on Circuits and Systems for Video Technology  (Volume:21 ,  Issue: 11 )