Objectionable Image Detection by ASSOM Competition ⋆

Abstract. This article presents a method aiming at filtering objectionable image contents. This kind of problem is very similar to object recognition and image classification. In this paper, we propose to use Adaptive-Subspace Self-Organizing Maps (ASSOM) to generate invariant pornographic features. To reach this goal, we construct local signatures associated to salient patches according to adult and benign databases. Then, we feed these vectors into each specialized ASSOM neural network. At the end of the learning step, each neural unit is tuned to a particular local signature prototype. Thus, each input image generates two neural maps that can be represented by two activation vectors. A supervised learning is finally done by a Normalized Radial Basis Function (NRBF) network to decide the image category. This scheme offers very promising results for image classification with a percentage of 87.8 % of correct classification rates.