Learning semantic distance from community-tagged media collection
Guo-Jun Qi, Xian‐Sheng Hua, Hong-Jiang Zhang · 2009
This paper proposes a novel semantic-aware distance met-ric for images by mining multimedia data on the Internet, in particular, web images and their associated tags. As well known, a proper distance metric between images is a key ingredient in many realistic web image retrieval engines, as well many image understanding techniques. In this pa-per, we attempt to mine a novel distance metric from the web images by integrating their visual content as well as the associated user tags. Different from many existing dis-tance metric learning algorithms which utilize the dissimilar or similar information between images pixels or features in signal level, the proposed scheme also takes the associated user-input tags into consideration. The visual content of images is also leveraged to respect an intuitive assumption that the visual similar images ought to have a smaller dis-tance. A semi-definite programming is formulated to encode the above two aspects of criteria to learn the distance met-ric and we show such an optimization problem can be effi-ciently solved with a closed-form solution. We evaluate the proposed algorithm on two datasets. One is the benchmark Corel dataset and the other is a real-world dataset crawled from the image sharing website Flickr. By comparison with other existing distance learning algorithms, competitive re-sults are obtained by the proposed algorithm in experiments.