Large Scale Online Learning of Image Similarity through Ranking

ent abstract presents OASIS, an Online Algorithm for Scalable Image Similarity learning that learns a bilinear similarity measure over sparse representations. OASIS is an online dual approach using the passive-aggressive family of learning algorithms with a large margin criterion and an efficient hinge loss cost. Our experiments show that OASIS is both fast and accurate at a wide range of scales: for a dataset with thousands of images, it achieves better results than existing state-of-the-art methods, while being an order of magnitude faster. Comparing OASIS with different symmetric variants, provides unexpected insights into the effect of symmetry on the quality of the similarity. For large, web scale, datasets, OASIS can be trained on more than two million images from 150K text queries within two days on a single CPU. Human evaluations showed that 35% of the ten top images ranked by OASIS were semantically relevant to a query image. This suggests that query-independent similarity c...

• Type: Conference
• Year: 2009
• Where: IBPRIA
• Authors: Gal Chechik, Varun Sharma, Uri Shalit, Samy Bengio
• Keywords: IBPRIA 2009, Image, OASIS, Pattern Recognition, Similarity Measure