Pornographic images recognition based on spatial pyramid partition and multi-instance ensemble learning

For tackling the problem of pornographic image recognition, a novel multi-instance learning (MIL) algorithm is proposed by using extreme learning machine (ELM) and classifiers ensemble. Firstly, a spatial pyramid partition-based (SPP) multi-instance modeling technique has been deployed to transform the pornographic images recognition problem into a typical MIL problem. The method has deployed a bag corresponding to an image and an instance corresponding to each partitioned sub-block described by low-level visual features (i.e. color, texture and shape). Secondly, a collection of visual word (VW) has been generated by using hierarchical k-mean clustering method, and then based on the fuzzy membership function between instance and VW, a fuzzy histogram fusion-based metadata calculation method has been proposed to convert each bag to a single sample, which allows the MIL problem to be solved directly by a standard single instance learning (SIL) machine. Finally, by using ELM, a group of base classifiers with different number of hidden nodes have been constructed, and their weights bas been dynamically determined by using performance weighting rule. Therefore, the strategy of classifiers ensemble is used to improve the overall adaptability of proposed ELMCE-MIL algorithm. Experimental results have shown that the method is robust, and its performance is superior to other similar algorithms.