Scale-Free Content Based Image Retrieval (or Nearly so)
Résumé
When textual annotations of Web and social media images are poor or missing, content-based image retrieval is an interesting way to access them. Finding an optimal trade-off between accuracy and scalability for CBIR is challenging in practice. We propose a retrieval method whose complexity is nearly independent of the collection scale and does not degrade results quality. Images are represented with sparse semantic features that can be stored as an inverted index. Search complexity is drastically reduced by (1) considering the query feature dimensions independently and thus turning search into a concatenation operation and (2) pruning the index in function of a retrieval objective. To improve precision, the inverted index look-up is complemented with an exhaustive search over a fixed size list of intermediary results. We run experiments with three public collections and results show that our much faster method slightly outperforms an exhaustive search done with two competitive baselines.