Estimating Missing Features to Improve Multimedia Information Retrieval

Retrieval in a multimedia database usually involves combining information from different modalities of data, such as text and images. However, all modalities of the data may not be available to form the query. The retrieval results from such a partial query are often less than satisfactory. In this paper, we present an approach to complete a partial query by estimating the missing features in the query. Our experiments with a database of images and their associated captions show that, with an initial text-only query, our completion method has similar performance to a full query with both image and text features. In addition, when we use relevance feedback, our approach outperforms the results obtained using a full query

Adaptive Clustering: Better Representatives with Reinforcement Learning

Adaptive clustering uses reinforcement learning to learn the reward values of successive data clusterings. Adaptive clustering applies when external feedback exists for a clustering task. It supports the reuse of clusterings by memorizing what worked well in a previous context. It explores multiple paths in a reinforcement learning environment when the goal is to find better cluster representatives based on arbitrary environmental feedback. Our experiments apply adaptive clustering to instance-based learning relying on a distance function modification approach. The results show that adaptive clustering can find better representatives, if compared with traditional instance-based learning, such as k-nearest neighbor classifiers. Moreover, we introduce as a by-product a new instance-based learning technique that classifies examples by solely using cluster representatives; the technique shows high promise in our experimental evaluation