Comparative Analysis for Bag of Features (BoF) Performance

Abstract

The accuracy of the Bag of Features (BoF) is greatly affected by the discriminatory power of feature extraction techniques. This paper presented a proposed method designed to find the best features technique for constructing a BoF model according to the image classification. It consists of four stages: feature extraction, where detectors and descriptor feature techniques have been exploited to generate different BoF models. Each BoF model is generated depending on what detector and descriptor are used. The BoF models are constructed to represent the images as feature vectors. The classification process is then performed on two image datasets.  Finally, the efficiency of BoF models is analyzed and evaluated with respect to the accuracy of their classification performance. Experimental results indicated that the best level of accuracy was provided by the proposed BoF model with the KAZE features method. The results also showed that the BoF model with Speeded Up Robust Features (SURF) was superior to other feature methods  in terms of execution time, which was 0.01218 seconds. Moreover, the BoF model generated by the SURF detector combined with the KAZE descriptor achieved a high level of accuracy of 0.99 and kept the time complexity low (0.01948 seconds).