SBIR Phase I: Automated Image Annotation

This Small Business Innovation Research (SBIR) Phase I project examines the feasibility of automatically assigning keywords to new images using instance-based learning methods, content analysis and pre-annotated images. This project will provide a new color quantization method that describes images using a common set of representative and discriminatory colors. This results in a reduction of the number of calculations required by the distance metrics employed by instance-based methods in determining which keywords to assign. This project will also investigate the utility of transforming the low-level color domain into a new feature domain, effectively removing the correlations between color features. This reduces the potential for annotation error when the instance-based methods employ distance metrics that does not account for correlations. Users implicitly refine these initial annotations when they perform searches using relevance feedback. By examining the user feedback, the relative importance of the keywords assigned to an image is modified. The more relevant keywords are assigned greater weights and the less relevant smaller weights. This permits erroneously assigned keywords to be effectively ignored. Hence, this system provides an efficient means for automatically assigning keywords to images and allows for automatic corrections by incorporating the results of user searches. This project will provide organizations that are involved in image generation and/or collection to easily and inexpensively annotate new images. Savings are achieved in both the monetary and efficiency arenas. By automating the annotation process, the need for a staff dedicated to examining and categorizing raw data is greatly reduced, resulting in reduction of costs. Furthermore, by automating the process, the speed at which annotations can be assigned is enhanced, allowing for greater throughput. In addition, by annotating the images, keyword-based search and retrieval systems can now be used on the organization's image collection, allowing for greater leverage of existing software products and permitting greater exposure and utilization of the collection.