Towards semantic knowledge propagation from text corpus to web images
Guo-Jun Qi, Charų C. Aggarwal, Thomas S. Huang · 2011
In this paper, we study the problem of transfer learning from text to images in the context of network data in which link based bridges are available to transfer the knowledge between the different domains. The problem of classification of image data is often much more challenging than text data because of the following two reasons: (a) Labeled text data is very widely available for classification purposes. On the other hand, this is often not the case for image data, in which a lot of images are available from many sources, but many of them are often not labeled. (b) The image features are not directly related to semantic concepts inherent in class labels. On the other hand, since text data tends to have natural semantic interpretability (because of their human origins), they are often more directly related to class labels. Therefore, the relationships between the images and text features also provide additional hints for the classification process in terms of the image feature transformations which provide the most effective results.