Abstract
We present a conditional probabilistic framework for collaborative representation of image patches. It in-corporates background compensation and outlier patch suppression into the main formulation itself, thus doingaway with the need for pre-processing steps to handle the same. A closed form non-iterative solution of the costfunction is derived. The proposed method (PProCRC) outperforms earlier related patch based (PCRC, GP-CRC)as well as the state-of-the-art probabilistic (ProCRC and EProCRC) models on several fine-grained benchmarkimage datasets for face recognition (AR and LFW) and species recognition (Oxford Flowers and Pets) tasks.We also expand our recent endemic Indian birds (IndBirds) dataset and report results on it. The demo code andIndBirds dataset are available through lead author.
Abstract (translated)
我们提出了一个条件概率框架，用于图像补丁的协同表示。它将背景补偿和离群点抑制结合到主要公式中，从而满足了预处理步骤处理的需要。本文导出了科斯特函数的一个闭式非迭代解。所提出的方法（pprocrc）优于早期相关的基于补丁（pcrc，gp-crc）以及最新的概率（procrc和eprocrc）模型，在用于人脸识别（ar和lfw）和物种识别（牛津花和宠物）任务的多个细粒度基准图像数据集上。我们还扩展了最近流行的印度鸟类（indbi）。rds）数据集并报告结果。演示代码和birds数据集可通过主要作者获得。
URL
https://arxiv.org/abs/1903.09123
