An Interactive Approach to Integrating External Textual Knowledge for Multimodal Lifelog Retrieval

The semantic gap between textual queries and visual concepts is one of the key challenges in lifelog retrieval. This work presents an interactive system aimed at improving the retrieval accuracy by query term suggestion. Besides, this system also assists users to refine the retrieval results by image similarity clustering. For recommending a list of candidate words, we extract visual concepts from images by using computer vision models, and then incorporate both official and additional concepts into our system using pre-trained word embedding, in which textual knowledge is inherent. We also purpose an intelligent mechanism for rapidly removing multiple irrelevant search results. For reaching out this purpose, we build kd-trees [1] offline for reducing the computational overhead and cluster similar images by nearest neighbor search in the embedding space. Whenever users exclude some irrelevant images, their nearest neighbors in the image embedding space are also removed. In this way, users can efficiently screen out the relevant results and purge the irrelevant ones, scanning over more retrieval results in a shorter period of time.