MULTITHREADING ADABOOST FRAMEWORK FOR OBJECT RECOGNITION

Our research focuses on the study of effective feature description and robust classifier technique, proposing a novel learning framework, which is capable of processing multiclass objects recognition simultaneously and accurately. The framework adopts rotation-invariant histograms of oriented gradients (Ri-HOG) as feature descriptors. Most of the existing HOG techniques are computed on a dense grid of uniformly-spaced cells and use overlapping local contrast of rectangular blocks for normalization. However, we adopt annular spatial bins type cells and apply the radial gradient to attain gradient binning invariance for feature extraction. In this way, it significantly enhances HOG in regard to rotation-invariant ability and feature description accuracy; The classifier is derived from AdaBoost algorithm, but it is ameliorated and implemented through non-interfering boosting channels, which are respectively built to train weak classifiers for each object category. In this way, the boosting cascade can allow the weak classifier to be trained to fit complex distributions. The proposed method is valid on PASCAL VOC 2007 database and it achieves the state-of-the-arts performance.