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An Idea-supporting Multimedia Search System
An information retrieval system that organically links images, video and other data to help searchers find inspiration and ideas.
The idea-supporting multimedia search system organically links unstructured data such as images, music and video, extracts inherent similarities and effectively presents them to searchers to help them find ideas and inspiration.
Research
We have succeeded in establishing associations and similarities between different media, and developed an associative search scheme that takes ambiguity of multimedia information into consideration (fused search). We have also realized a new search engine and interface by quickly introducing modeling of personal preferences through user networks and visualization of similarities in preferences through user interfaces (personal adaptive search). Use of the search engine and interface enables a completely new search that effectively utilizes the polysemy and ambiguity inherent in multimedia contents.
Miki Haseyama Professor -
Multimedia Artificial Intelligence Technology Reaching Social Implementation
Approaching the practical application of AI technology through industry-university collaborative research!
With this research, we are developing artificial intelligence technology for multimedia data, mainly images, video, music, and audio. We are handling data related to medical images, social infrastructure data, materials science and other fields, mainly through industry-university collaborative research.
Research
We are not only conducting the world's most advanced artificial intelligence research, but also promoting research in interdisciplinary areas and taking on the challenge of solving real-world problems. Specifically, in medical imaging research, we have collaborated with many medical institutions in Japan to build AI technology that surpasses human diagnostic accuracy. In medical and civil engineering research, we have built Explainable AI (XAI), which not only enables learning of small amounts of data, a challenge in AI research, but also enables explanations of judgment results, making the technology usable in the real world. In recent years, we have also developed human-centric AI technology that can make decisions like humans by introducing information strongly related to human interests, such as human brain activity and eye gaze data, into the AI learning process.
Takahiro Ogawa Professor
