In this article, a general framework of image annotation is proposed by involving salient object detection (SOD), feature extraction, feature selection, and multi-label classification. For SOD, Augmented-Gradient Vector Flow (A-GVF) is proposed, which fuses benefits of GVF and Minimum Directional Contrast. The article also proposes to control the background information to be included for annotation. This article brings about a comprehensive study of all major feature selection methods for a study on four publicly available datasets. The study concludes with the proposition of using Fisher’s method for reducing the dimension of features. Moreover, this article also proposes a set of features that are found to be strong discriminants by most of the methods. This reduced set for image annotation gives 3--4% better accuracy across all the four datasets. This article also proposes an improved multi-label classification algorithm C-MLFE.

中文翻译：

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高效图像标注框架的设计、分析和实现

在本文中，提出了一个图像标注的通用框架，涉及显着对象检测（SOD）、特征提取、特征选择和多标签分类。对于 SOD，提出了增强梯度向量流 (A-GVF)，它融合了 GVF 和最小方向对比度的优点。文章还提出控制背景信息被包含在注释中。本文针对四个公开可用的数据集进行了对所有主要特征选择方法的全面研究。该研究最后提出了使用Fisher方法降低特征维度的提议。此外，本文还提出了一组特征，这些特征被大多数方法发现是强判别器。这个减少的图像注释集在所有四个数据集中提供了 3--4% 的更好的准确性。本文还提出了一种改进的多标签分类算法C-MLFE。