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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 requires no training, and it can easily scale to datasets with tens of thousands of images and hundreds of labels. It works by(More)
This paper presents a system for image parsing, or labeling each pixel in an image with its semantic category, aimed at achieving broad coverage across hundreds of object categories, many of them sparsely sampled. The system combines region-level features with per-exemplar sliding window detectors. Per-exemplar detectors are better suited for our parsing(More)
This work proposes a method to interpret a scene by assigning a semantic label at every pixel and inferring the spatial extent of individual object instances together with their occlusion relationships. Starting with an initial pixel labeling and a set of candidate object masks for a given test image, we select a subset of objects that explain the image(More)
(a) (b) Figure 1: (a) All detected features for a single image (b) Features filtered based on the results of geometric verification – only visual words that were inliers in at least 10 previous image pairs are shown. The features in (b) are heatmap colour coded based on the inlier counts. In this work, we address the issue of geometric verification, with a(More)
We consider the problem of estimating the relative depth of a scene from a monocular image. The dark channel prior, used as a statistical observation of haze free images, has been previously leveraged for haze removal and relative depth estimation tasks. However, as a local measure, it fails to account for higher order semantic relationship among scene(More)
This paper describes a system for interpreting a scene by assigning a semantic label at every pixel and inferring the spatial extent of individual object instances together with their occlusion relationships. First we present a method for labeling each pixel aimed at achieving broad coverage across hundreds of object categories, many of them sparsely(More)
In this work, we address the issue of geometric verification, with a focus on modeling large-scale landmark image collections gathered from the internet. In particular, we show that we can compute and learn descriptive statistics pertaining to the image collection by leveraging information that arises as a by-product of the matching and verification stages.(More)
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