Object classification and recognition using Bag-of-Words (BoW) model

Toward Performing Image Classification and Object Detection With Convolutional Neural Networks in Autonomous Driving Systems: A Survey

“…Looking back over the last two decades of detection methods, until the early 2010s the limitations on computing resources, datasets, and the mostly theoretical nature of DNN development had led to employing traditional detectors, such as DPM [106], Selective Search [107], Oxford-MKL [108], HOG [109], NLPR-HOGLBP [110], SIFT [111], VJ Det [112], Bag of Words [113], etc. A modular data-flow diagram and a historical publication timeline of these popular detectors are depicted in Fig.…”

Vision-Based Human Detection Techniques: A Descriptive Review

“…The "part filters" compute features two times of the spatial resolution of the "root filter". Here "root filter" [94], [102], object recognition [103], text classification [104], image retrieval [105] • quite simple to comprehend and implement [94] • can categorize the objects [94] • computationally expensive [95] • skips geometric relationships among visual words [96] • less annotation accuracy [97] SPM object recognition [106], image classification [98], [107] • computationally effective [98] • improves the classification accuracy [98] • weight's mechanism is not sophisticated [99] • insufficient discriminative power [100] BOVW scene classification [108], [108], land-use classification [109], object classification [110] • improves the classification accuracy [108] • visual vocabulary's computational cost is high [111] behaves similar to a Dalal-Triggs method. The outcome of this method at a specific point and scale in a picture is the result of the "root filter" on the window plus the sum over parts, of the limit over positions of that component, of the "part filter" record on the coming from sub-window minus the cost of deformation.…”

Improvement of the inspection-repair process with building information modelling and image classification

“…Bag of Words method was selected to conduct image classification because of its simplicity, maturity and practical performance (Nowak et al, 2006) in various applications. In addition, classifying different objects is one of the main objectives of this system, and recent studies have proven that Bag of Words is an effective and robust approach to object classification (Ali et al, 2016;Hannat et al, 2016;Vyas et al, 2016;Ergene and Durdu, 2017;Nguyen-Hoang et al, 2017;Unlu et al, 2017).…”