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

Abstract

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.