A Review on Image Classification Using Bag of Features Approach
DOI:
https://doi.org/10.26438/ijcse/v7i6.538542Keywords:
Bag Of Features, Feature Extraction, Quantization, Clustering, Image Representation, Image Classification, Support Vector MachineAbstract
Bag of Features or BoF approach has been used in many computer vision tasks, including image classification, video search, robot localization, and texture recognition. It is so widely popular because of its simplicity. These methods are based on unordered collections of image descriptors which are then quantized and are discarded spatial information, therefore conceptually and computationally simpler than many alternative methods, because of this; BoF based systems have set new performance standards on popular image classification benchmarks and have achieved scalability breakthroughs in image retrieval. This paper reviews related works based on the issues of improving and/or applying BoF. Emphasis is placed on recent techniques that mitigate quantization errors, improve feature detection, and speed up image retrieval. Meanwhile, unresolved issues and fundamental challenges are also raised. Among those issues the best techniques for sampling images, describing local image features, and evaluating system performance. Among those the fundamental challenges are how the BoF methods can contribute in localizing the objects in more complex images, or associating high-level semantics with natural images. Moreover, many recent works are compared in terms of the methodology of BoF feature generation and experimental design. Different Classification Models are also discussed.
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