Automatic object annotation from weakly labeled data with latent structured SVM

Christian X. Ries, Fabian Richter, Stefan Romberg, R. Lienhart
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引用次数: 4

Abstract

In this paper we present an approach to automatic object annotation. We are given a set of positive images which all contain a certain object and our goal is to automatically determine the position of said object in each image. Our approach first applies a heuristic to identify initial bounding boxes based on color and gradient features. This heuristic is based on image and feature statistics. Then, the initial boxes are refined by a latent structured SVM training algorithm which is based on the CCCP training algorithm. We show that our approach outperforms previous work on multiple datasets.
基于潜在结构化支持向量机的弱标记数据对象自动标注
本文提出了一种自动对象标注的方法。我们给定一组包含某个物体的正图像,我们的目标是自动确定该物体在每个图像中的位置。我们的方法首先应用启发式方法来识别基于颜色和梯度特征的初始边界框。这种启发式算法基于图像和特征统计。然后,利用基于CCCP训练算法的潜在结构化SVM训练算法对初始盒进行细化。我们表明,我们的方法优于以前在多个数据集上的工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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