2016 International Computer Symposium (ICS) 2016
DOI: 10.1109/ics.2016.0075
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Efficient DPM-Based Object Detection Using Shift with Importance Sampling

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“…Machine learning algorithms focus on feature extraction and classifiers [92]. For feature extraction, techniques such as Histogram of Oriented Gradients [93][94][95][96][97][98][99][100], Local Binary Pattern [101][102][103][104][105][106][107], Deformable Part Model [108][109][110][111][112][113], and Aggregate Channel Feature (ACF) [114][115][116][117][118] are included. On the other hand, methods such as Support Vector Machine (SVM) [94,105,[119][120][121][122], Decision Tree [123][124][125][126], Random Forest (RF) [127][128][129][130][131][132] and Ada-Boost [81,119,133,134] are used for ...…”
Section: Object Detection and Classificationmentioning
confidence: 99%
“…Machine learning algorithms focus on feature extraction and classifiers [92]. For feature extraction, techniques such as Histogram of Oriented Gradients [93][94][95][96][97][98][99][100], Local Binary Pattern [101][102][103][104][105][106][107], Deformable Part Model [108][109][110][111][112][113], and Aggregate Channel Feature (ACF) [114][115][116][117][118] are included. On the other hand, methods such as Support Vector Machine (SVM) [94,105,[119][120][121][122], Decision Tree [123][124][125][126], Random Forest (RF) [127][128][129][130][131][132] and Ada-Boost [81,119,133,134] are used for ...…”
Section: Object Detection and Classificationmentioning
confidence: 99%