Joint Object Discovery And Segmentation With Image-Wise Reconstruction Error
2016 IEEE International Conference on Image Processing (ICIP)(2016)
摘要
We tackle the problem of joint discovery and segmentation of the object of interest from noisy image sets collected via web crawling (e.g., Figure 1). Existing methods [1] [2] [3] employ region-wise comparison in order to separate noise images (images not containing target objects) from the rest, which may be a bottleneck for scaling up to larger datasets. Our idea to avoid such computationally intensive operations is to use image-wise reconstruction errors. Specifically, based on the assumption that images containing target objects are easier to be reconstructed by a pool of target objects than noise images, we first reconstruct each image using a small number of similar target objects. The resulting error is then combined with some other criteria (e.g., saliency) so as to delineate only target object regions. Experimental evaluations on a noisy image dataset [1] demonstrate that our approach achieves state-of-the-art results on every subset of the dataset 5 - 7 times faster than existing methods.
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关键词
Joint object discovery and segmentation,cosegmentation,nonnegative sparse reconstruction
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