Part-based feature synthesis for human detection
2010
We introduce a new approach for learning part-based object detection through feature synthesis. Our method consists of an iterative process of feature generation and pruning. A feature generation procedure is presented in which basic part-based features are developed into a feature hierarchy using operators for part localization, part refining and part combination. Feature pruning is done using a new feature selection algorithm for linear SVM, termed Predictive Feature Selection (PFS), which is governed by weight prediction. The algorithm makes it possible to choose from O(106) features in an efficient but accurate manner. We analyze the validity and behavior of PFS and empirically demonstrate its speed and accuracy advantages over relevant competitors. We present an empirical evaluation of our method on three human detection datasets including the current de-facto benchmarks (the INRIA and Caltech pedestrian datasets) and a new challenging dataset of children images in difficult poses. The evaluation suggests that our approach is on a par with the best current methods and advances the state-of-the-art on the Caltech pedestrian training dataset.
Keywords:
- Machine learning
- Computer vision
- Feature selection
- Computer science
- Support vector machine
- Feature (computer vision)
- Competitor analysis
- Object detection
- Operator (computer programming)
- Pattern recognition
- Iterative and incremental development
- Artificial intelligence
- feature synthesis
- feature generation
- Hierarchy
- Correction
- Source
- Cite
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