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Searching consumer image collections using web-based concept expansion

As consumers accumulate more and more personal imagery, searching for specific images has become increasingly difficult. Consumers typically provide little or no annotations, and automated classifiers and concept tagging tools are limited in their scope and vocabulary. This work addresses this sparsity of semantic information by leveraging domain-specific information provided by online photo-sharing communities. Such information enables improved search by allowing user-provided search terms to be expanded into a set of semantically related concepts, using relevant semantic relationships provided by millions of users. Our system first extracts metadata using a modest number of image and event-based semantic classifiers, as well as any meaningful file or folder names. When users pose text-based queries, our system retrieves images from their personal image collections by leveraging Flickr's tag dataset for concept expansion. This approach enables users to search their collections without having to manually annotate their pictures. We compare the retrieval performance of using a Flickr-based concept expander with the performance obtained without concept expansion and with using a WordNet-based concept expander. The results demonstrate that common sense knowledge gleaned from online photo sharing communities can enable meaningful image search on consumer image collections, searches that would be impossible using only the available image metadata.