Partially Connected ELM for Fast and Effective Scene Classification

Abstract

Scene classification is often solved as a machine learning problem, where a classifier is first learned from training data, and class labels are then assigned to unlabelled testing data based on the outputs of the classifier. Generally, image descriptors are represented in high-dimensional space, where classifiers such as support vector machine (SVM) show good performance. However, SVM classifiers demand high computational power during model training. Extreme learning machine (ELM), whose synaptic weight matrix from the input layer to the hidden layer are randomly generated, has demonstrated superior computational efficiency. But the weights thus generated may not yield enough discriminative power for hidden layer nodes. Our recent study shows that the random mapping from the input layer to the hidden layer in ELM can be replaced by semi-random projection (SRP) to achieve a good balance between computational complexity and discriminative power of the hidden nodes. The application of SRP to ELM yields the so-called partially connected ELM (PC-ELM) algorithm. In this study, we apply PC-ELM to multi-class scene classification. Experimental results show that PC-ELM outperforms ELM in high-dimensional feature space at the cost of slightly higher computational complexity.