Image and Video Retrieval using a Visual Encyclopedia 9807-NTT 03 Proposal for 1998-1999 Funding
We propose a collaborative research project with NTT on image and video database search. We plan to study and create systems that can scan images and video to locate items of interest. For example, such a system should be able to scan a travel documentary for images of distinct locations and objects, like ``Buddhist temples'', ``gothic cathedrals'', or ``statues on horseback''. We believe that by leveraging our existing work in this area, we can play a key role in setting the standard for research in visual information retrieval. At the same time, this research provides an excellent opportunity for transition to practical applications. We believe that the complementary skills of MIT and NTT are well suited for pursuing this dual path of developing practical applications of image indexing in conjunction with fundamental progress in associated science and engineering. Image and Video Search As confidence in the Web as a marketplace for information increases, there has been an associated explosion of content on the Web. In the very near future, images, video and virtual reality will be available on demand much as text is now. Indexing into image and video content will require very different techniques than those currently associated with text, however. This follows both from the huge volume of information associated with imagery, and from the need to develop the visual equivalent of textual ``keywords'' to support indexing based on image content. Currently there are two prototypical schemes for searching text on the web: Altavista and Yahoo. Altavista provides a flat interface to the web; all documents which are similar to the query are returned. Yahoo presents an hierarchical organization of the web; documents are clustered by topic and arranged so that similar documents are nearby in the hierarchy. When it works well, Yahoo provides an important service: from a few key words documents from an entire subject area can be retrieved. Though this class of documents might have little surface similarity, by retrieving a single document from the class the entire class can be identified. Previous approaches to image database search have taken the "altavista" approach, wherein the image database lacks any organization. In response to a query, all images which are similar to the query are returned. We propose to construct an organized visual encyclopedia in which images are clustered and organized into a hierarchy. Like Yahoo our system should be able to take an under-specified query and return an entire class of images. We argue that an organized visual encyclopedia will address one of the greatest weaknesses of current image database systems: visual experience and memory. Without this memory it will be impossible for a computer retrieval system to match the performance of a human being. Given two pictures of dogs, a small black dog and a large spotted dog, the human observer has very little difficulty determining that these are dogs and generalizing to many other types of dogs. A computer system might return other four legged creatures, but it has no hope of rejecting cats or horses. There is simply not enough information in these images. The missing component is visual experience. People encounter many hundreds of dogs, having seen them from every conceivable angle. Given two examples, the human observer can immediately retrieve much of this visual experience, using it to correctly determine detailed information about the class of dogs. Our visual memory is likely to include useful negative examples such as cats and horses, which can be used to further refine our notion. All of this information is used when a human searches an image database. On the contrary the computer must work from only two example images. A visual encyclopedia can supply some of this missing information. The encyclopedia will likely have an entry for dogs containing hundreds of examples, pointers to similar negative examples like cats, and pointers to distant negative examples like whales. Clearly such an encyclopedia can have significant impact on image database retrieval. Before retrieving from a database, the encyclopedia can be used to both expand and refine a query. In addition to developing the technology for constructing and using a visual encyclopedia we intend to address several generic questions in image and video database retrieval. How can the system be made more reliable and easier to use? How can the system be made more efficient? How can the class of capabilities be extended to new domains? At the same time, by working with colleagues from NTT, we will leverage our ongoing work into practical demonstrations and applications.