Uniform Detection in Social Image Streams

Social media mining from Internet has been an emerging research topic. The problem is challenging because of massive data contents from various sources, especially image data from user upload. In recent years, dictionary learning based image classification has been widely studied and gained significant success. In this paper, we propose a framework for automatic detection of interested uniforms in image streams from social networks. The systems is composed of a powerful feature extraction module based on dense SIFT feature and a state-of-the-art discriminative dictionary learning approach. Beside that, a parallel implementation of feature extraction is deployed to make the system work real time. An extensive set of experiments has been conducted on four real-life datasets. The experimental results show that we can obtain the detection rate up to 100% on some datasets. We also get real time performance with a speed of image stream of about 40 images per second. The framework can be applied to emerging applications such as uniform detection, automated image tagging, content base image retrieval or online advertisement based on image content.