• superparsing

    جزئیات بیشتر مقاله
    • تاریخ ارائه: 1392/07/24
    • تاریخ انتشار در تی پی بین: 1392/07/24
    • تعداد بازدید: 1041
    • تعداد پرسش و پاسخ ها: 0
    • شماره تماس دبیرخانه رویداد: -
     this paper presents a simple and effective nonparametric approach to the problem of image parsing, or labeling image regions (in our case, superpixels produced by bottom-up segmentation) with their categories. this approach is based on lazy learning, and it can easily scale to datasets with tens of thousands of images and hundreds of labels. given a test image, it first performs global scene-level matching against the training set, followed by superpixel-level matching and efficient markov random field (mrf) optimization for incorporating neighborhood context. our mrf setup can also compute a simultaneous labeling of image regions into semantic classes (e.g., tree, building, car) and geometric classes (sky, vertical, ground). our system outperforms the state-of-the-art nonparametric method based on sift flow on a dataset of 2,688 images and 33 labels. in addition, we report per-pixel rates on a larger dataset of 45,676 images and 232 labels. to our knowledge, this is the first complete evaluation of image parsing on a dataset of this size, and it establishes a new benchmark for the problem. finally, we present an extension of our method to video sequences and report results on a video dataset with frames densely labeled at 1 hz.

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