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The Bag-of-Words Method with Different Types of Image Features and Dictionary Analysis

机译：具有不同类型图像特征的词袋法和字典分析

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Algorithms from the field of computer vision are widely applied in various fields including security, monitoring, automation elements, but also in multimodal human-computer interactions where they are used for face detection, body tracking and object recognition. Designing algorithms to reliably perform these tasks with limited computing resources and the ability to detect the presence of nearby people and objects in the background, changes in illumination and camera pose is a huge challenge for the field. Many of these problems use different classification methods. One of many image classification algorithms is Bag-of-Words (BoW). Originally, the classic BoW algorithm was used mainly for the natural language, so its direct application to computer vision issues may not be effective enough. The algorithm presented in this article contains a number of modifications that facilitate application of many types of characteristic features extracted from an image, image representation analysis and an adaptive clustering algorithm to create a dictionary of image features. These modifications affect classification result, which was confirmed in the experimental research.
机译：来自计算机视觉领域的算法被广泛应用于各个领域，包括安全性，监视，自动化元素，还应用于多模式人机交互，这些算法用于面部检测，人体跟踪和物体识别。设计算法以有限的计算资源可靠地执行这些任务，并具有检测背景中附近人物和物体的存在，照明和相机姿势变化的能力，对于该领域来说是巨大的挑战。这些问题中有许多使用不同的分类方法。词袋（BoW）是许多图像分类算法之一。最初，经典的BoW算法主要用于自然语言，因此将其直接应用于计算机视觉问题可能不够有效。本文介绍的算法包含许多修改，这些修改有助于应用从图像中提取的多种类型的特征，图像表示分析和自适应聚类算法以创建图像特征字典。这些修改影响了分类结果，这在实验研究中得到了证实。

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