Object class recognition using multiple layer boosting with heterogeneous features

We combine local texture features (PCA-SIFT), global features (shape context), and spatial features within a single multi-layer AdaBoost model of object class recognition. The first layer selects PCA-SIFT and shape context features and combines the two feature types to form a strong classifier. Although previous approaches have used either feature type to train an AdaBoost model, our approach is the first to combine these complementary sources of information into a single feature pool and to use Adaboost to select those features most important for class recognition. The second layer adds to these local and global descriptions information about the spatial relationships between features. Through comparisons to the training sample, we first find the most prominent local features in Layer 1, then capture the spatial relationships between these features in. Layer 2. Rather than discarding this spatial information, we therefore use it to improve the strength of our classifier. We compared our method to [4, 12, 13] and in all cases our approach outperformed these previous methods using a popular benchmark for object class recognition [4]. ROC equal error rates approached 99%. We also tested our method using a dataset of images that better equates the complexity between object and non-object images, and again found that our approach outperforms previous methods.