Non-parametric spatially constrained local prior for scene parsing on real-world data

• URL: http://arxiv.org/abs/2006.12874v1
• Date: Tue, 23 Jun 2020 10:12:08 GMT
• Title: Non-parametric spatially constrained local prior for scene parsing on real-world data
• Authors: Ligang Zhang
• Abstract summary: We present the non-parametric Spatially Constrained Local Prior (SCLP) for scene parsing on realistic data. The SCLP is powerful in capturing both long- and short-range context about inter-object correlations in the query image. Our experiments on the SIFT Flow and PASCAL-Context benchmark datasets show that the SCLP used in conjunction with superpixel-level visual features achieves one of the top performance compared with state-of-the-art approaches.
• Score: 4.035753155957698
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scene parsing aims to recognize the object category of every pixel in scene images, and it plays a central role in image content understanding and computer vision applications. However, accurate scene parsing from unconstrained real-world data is still a challenging task. In this paper, we present the non-parametric Spatially Constrained Local Prior (SCLP) for scene parsing on realistic data. For a given query image, the non-parametric SCLP is learnt by first retrieving a subset of most similar training images to the query image and then collecting prior information about object co-occurrence statistics between spatial image blocks and between adjacent superpixels from the retrieved subset. The SCLP is powerful in capturing both long- and short-range context about inter-object correlations in the query image and can be effectively integrated with traditional visual features to refine the classification results. Our experiments on the SIFT Flow and PASCAL-Context benchmark datasets show that the non-parametric SCLP used in conjunction with superpixel-level visual features achieves one of the top performance compared with state-of-the-art approaches.

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