Large-Scale Image Mining with Flickr Groups

The availability of large annotated visual resources, such as ImageNet, recently led to important advances in image mining tasks. However, the manual annotation of such resources is cumbersome. Exploiting Web datasets as a substitute or complement is an interesting but challenging alternative. The main problems to solve are the choice of the initial dataset and the noisy character of Web text-image associations. This article presents an approach which first leverages Flickr groups to automatically build a comprehensive visual resource and then exploits it for image retrieval. Flickr groups are an interesting candidate dataset because they cover a wide range of user interests. To reduce initial noise, we introduce innovative and scalable image reranking methods. Then, we learn individual visual models for 38,500 groups using a low-level image representation. We exploit off-the-shelf linear models to ensure scalability of the learning and prediction steps. Finally, Semfeat image descr...

• Type: Journal
• Year: 2015
• Where: MMM
• Authors: Alexandru-Lucian Gînsca, Adrian Popescu 0001, Hervé Le Borgne, Nicolas Ballas, Phong Vo, Ioannis Kanellos
• Keywords: MMM 2015, Multimedia