원문정보
초록
영어
It is the important that Support Vector Machine (SVM) is the powerful learning machines and has been applied to varying task with generally acceptable performance. The SVM success for classification tasks in one domain is affected by features that it represents the instance of specific class. The representative and discriminative features that they are given, SVM learning is going to provide better generalization and consequently that we are able to obtain good classifier. In this paper, we define the problem of feature choices for tasks of human detections and measure the performance of each feature. And also we consider HOG-family feature to study an effective feature selection method. Finally we proposed the multi-scale HOG as a NEW family member in this feature group. In addition we also combine SVM with Principal Component Analysis (PCA) to reduce dimension of features and enhance the evaluation speed while retaining most of discriminative feature vectors.
목차
1. Introduction
2. Features and the Classifier
2.1. Second-order Headings
2.2. Building HOG from Image
2.3. Support Vector Machine
2.4. SVM Evaluation with PCA
3. Experiments
3.1. Experimental Results
4. Discussion
5. Conclusion and Future Direction
Acknowledgements
References