GRASP Recurring Patterns from a Single View

Jingchen Liu, Yanxi Liu
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引用次数: 33

Abstract

We propose a novel unsupervised method for discovering recurring patterns from a single view. A key contribution of our approach is the formulation and validation of a joint assignment optimization problem where multiple visual words and object instances of a potential recurring pattern are considered simultaneously. The optimization is achieved by a greedy randomized adaptive search procedure (GRASP) with moves specifically designed for fast convergence. We have quantified systematically the performance of our approach under stressed conditions of the input (missing features, geometric distortions). We demonstrate that our proposed algorithm outperforms state of the art methods for recurring pattern discovery on a diverse set of 400+ real world and synthesized test images.
从单一视图中把握重复模式
我们提出了一种新的无监督方法,用于从单个视图中发现重复模式。我们的方法的一个关键贡献是联合分配优化问题的制定和验证,其中同时考虑潜在重复模式的多个视觉单词和对象实例。优化是通过贪婪随机自适应搜索程序(GRASP)实现的,该程序的移动是专门为快速收敛而设计的。我们系统地量化了我们的方法在输入的压力条件下的性能(缺失特征,几何扭曲)。我们证明了我们提出的算法在400多个真实世界和合成测试图像的不同集合上的重复模式发现优于最先进的方法。
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