Compact Feature Representation for Image Classification Using ELMs

Abstract

Feature representation/learning is an essential step for many computer vision tasks (like image classification) and is broadly categorized as 1) deep feature representation; 2) shallow feature representation. With the development of deep neural networks, many deep feature representation methods have been proposed and obtained many remarkable results. However, they are limited to real-world applications due to the high demand for storage space and computation ability. In our work, we focus on shallow feature representation (like PCANet) as these algorithms require less storage space and computational resources. In this paper, we have proposed a Compact Feature Representation algorithm (CFR-ELM) by using Extreme Learning Machine (ELM) under a shallow network framework. CFR-ELM consists of compact feature learning module and a post-processing module. Each feature learning module in CRF-ELM performs the following operations: 1) patch-based mean removal; 2) ELM auto-encoder (ELM-AE) to learn features; 3) Max pooling to make the features more compact. Post-processing module is inserted after the feature learning module and simplifies the features learn by the feature learning modules by hashing and block-wise histogram. We have tested CFR-ELM on four typical image classification databases, and the results demonstrate that our method outperforms the state-of-the-art methods.