An Improved Multiple Instance Learning Algorithm for Object Extraction

Based on MILES algorithm, we propose a novel multiple instance learning approach which regards visual word dictionary as feature space, and combines segmentation for object detection and extraction in the process of instance classification. This approach uses "Bag of Words" model. The whole image is considered as a multiple instance bag. The visual words that represent the image are regarded as the instances in the bag. The approach maps each bag into a feature space defined by visual vocabulary via the histogram over visual words. Next, 1-norm SVM is applied to select important features as well as classify images simultaneously. Then we will classify instances coming from the bags classified as positive, and take the positive instances for object "seed" points. After that segmentation is combined to realize object extraction. Experiments on Caltech101 dataset show that this approach achieves high efficiency.