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In this paper, we propose a shape-guided segmentation algorithm for fine-grained visual classification(FGVC). First, edge information is extracted from the query image and compared with each sample of training set, which can help us retrieve a subset of candidate proposals. These proposals are used to learn prior shape knowledge by separately estimating the foreground probabilities of corresponding...
In this paper, we proposed a segmentation approach that not only segment an interest object but also label different semantic parts of the object, where a discriminative model is presented to describe an object in real world images as multiply, disparate and correlative parts. We propose a multi-stage segmentation approach to make inference on the segments of an object. Then we train it under the...
In this paper, we present a novel generalized Segment-Forest Model (SFM) to segment an object as well as label all the object's semantic parts simultaneously. Segment-Forest is composed by various generated segment trees that act directly on super pixels. Unlike recent works, SFM does not need the prior information like skeleton to capture the core structure of an object, but actively learns the structure...
In the last few years, substantially different approaches have been adopted for segmenting and detecting “things” (object categories that have a well defined shape such as people and cars) and “stuff” (object categories which have an amorphous spatial extent such as grass and sky). While things have been typically detected by sliding window or Hough transform based methods, detection of stuff is generally...
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