Submitted to Special Issue of Pattern Recognition on Image Database: Classiication and Retrieval Interactive Learning Using a \society of Models"
Digital library access is driven by features, but features are often context-dependent and noisy, and their relevance for a query is not always obvious. This paper describes an approach for utilizing many data-dependent, user-dependent, and task-dependent features in a semi-automated tool. Instead of requiring universal similarity measures or manual selection of relevant features, the approach provides a learning algorithm for selecting and combining groupings of the data, where groupings can be induced by highly specialized and context-dependent features. The selection process is guided by a rich example-based interaction with the user. The inherent com-binatorics of using multiple features is reduced by a multistage grouping generation, weighting, and collection process. The stages closest to the user are trained fastest and slowly propagate their adaptations back to earlier stages. The weighting stage adapts the collection stage's search space across uses, so that, in later interactions, good groupings are found given few examples from the user. Described is an interactive-time implementation of this architecture for semi-automatic within-image segmentation and across-image labeling , driven by concurrently active color models , texture models, or manually-provided groupings. 1 Issues for digital libraries Digital libraries of images, video, and sound are a rich area for pattern recognition research. They also introduce a host of new problems and requirements, since the range of possible queries is immense and requires the utilization of many specialized features. Also, systems for retrieval, browsing, and annotation, i.e. classifying regions, often must perform with only a small number of examples from a user, i.e. an insuf-cient amount of training data by traditional requirements. Thus the area is doubly exciting since it presents the eld of pattern recognition with new challenges while beckoning in new applications. One important issue for digital libraries is nding good models and similarity measures for comparing database entries. A part of this diiculty is that feature extraction and comparison methods are highly data-dependent; see Figure 2 for an example with texture. Similarity measures are also user and task dependent, as demonstrated by Figure 3. Unfortunately , these dependencies are not, at this point, understood well enough, especially by the typical digital library user, to permit careful selection of the optimal measure beforehand. Note that the multi-resolution simultaneous auto-regressive (MRSAR) model of 1], which fares poorly compared to the shift-invariant eigenvector (EV) model in the above two examples, scores clearly above the EV model on the standard Brodatz database 2] …