Improving feature-based object recognition for X-ray baggage security screening using primed visualwords

We present a novel Bag-of-Words (BoW) representation scheme for image classification tasks, where the separation of features distinctive of different classes is enforced via class-specific feature-clustering. We investigate the implementation of this approach for the detection of firearms in baggage security X-ray imagery. We implement our novel BoW model using the Speeded-Up Robust Features (SURF) detector and descriptor within a Support Vector Machine (SVM) classifier framework. Experimentation on a large, diverse data set yields a significant improvement in classification performance over previous works with an optimal true positive rate of 99.07% at a false positive rate of 4.31%. Our results indicate that class-specific clustering primes the feature space and ultimately simplifies the classification process. We further demonstrate the importance of using diverse, representative data and efficient training and testing procedures. The excellent performance of the classifier is a strong indication of the potential advantages of this technique in threat object detection in security screening settings.