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 featureclustering. 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.
Recent research has demonstrated the great capability of deep belief networks for solving a variety of visual recognition tasks. However, primary focus has been on modelling higher level visual features and later stages of visual processing found in the brain. Lower level processes such as those found in the retina have gone ignored. In this paper, we address this issue and demonstrate how the retina's inherent multi-layered structure lends itself naturally for modelling with deep networks. We introduce a method for simulating the retinal photoreceptor input and show the efficacy of deep networks in learning feature detectors similar to retinal ganglion cells. We thereby demonstrate the potential of deep belief networks for modelling the earliest stages of visual processing.
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