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Environment Scene Classification Based on Images Using Bag-of-Words

机译：基于词袋的图像环境场景分类

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We analyse the environment scene classification methods based on the Bag of Words (BoW) model. The BoW model encodes images by a bag of visual features, which is a sparse histogram over a dictionary of visual features extracted from an image. We analyse five feature detectors (Scale Invasive Feature Transform (SIFT), Speed-Up Robust Features (SURF), Features from Accelerated Segment Test (FAST), Maximally Stable Extremal Regions (MSER), and grid-based) and three feature descriptors (SIFT, SURF and U-SURF). Our experiments show that feature detection with a grid and feature description using SIFT descriptor, and feature detection with SURF and feature description with U-SURF are most effective when classifying (using Support Vector Machine (SVM)) images into eight outdoor scene categories (coast, forest, highway, inside city, mountain, open country, street, and high buildings). Indoor scene classification into five categories (bedroom, industrial, kitchen, living room, and store) achieved worse results, while the most confused categories were industrial/store images. The classification of full image dataset (15 outdoor and indoor categories) achieved the overall accuracy of 67.49 ± 1.50%, while most errors came from misclassifications of indoor images. The results of the study can be applicable for assisting living applications and security systems.
机译：我们分析了基于词袋（BoW）模型的环境场景分类方法。 BoW模型通过一袋视觉特征对图像进行编码，这是从图像中提取的视觉特征字典上的稀疏直方图。我们分析了五个特征检测器（比例侵入特征变换（SIFT），加速鲁棒特征（SURF），加速段测试（FAST）的特征，最大稳定的末端区域（MSER）和基于网格的特征）和三个特征描述符（ SIFT，SURF和U-SURF）。我们的实验表明，在将图像分类（使用支持向量机（SVM））分为八个室外场景类别（海岸）时，使用网格进行特征检测和使用SIFT描述符进行特征描述，使用SURF进行特征检测以及使用U-SURF进行特征描述最有效。 ，森林，高速公路，城市内部，山脉，空旷的国家，街道和高层建筑物）。室内场景分为五类（卧室，工业，厨房，客厅和商店）取得了较差的效果，而最混乱的类别是工业/商店图像。完整图像数据集的分类（15个室外和室内类别）的总体准确度为67.49±1.50％，而大多数错误来自室内图像的错误分类。研究结果可适用于辅助生活应用程序和安全系统。

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