Efficient Indexing of Multiple Metric Spaces with Spectra

The widespread of social networks and online channels has increased the capture of large amounts of complex data, such as images and videos, which demand efficient and flexible tools to perform information retrieval. Many existing approaches to retrieve complex data follow the "Query by Similarity" paradigm, using Metric Access Methods (MAMs) to index complex data and speed-up information retrieval. In this context, many descriptors represent complex data using representative features such as color, shape, or texture for images. MAMs were initially designed to index features from complex data using only one descriptor, leading users to build several indexes when more than one descriptor is required. Recent approaches that use different representations in a single index structure suffer from a higher number of distance calculations. In this work, we propose the Spectra MAM, which indexes complex data using several features at once. Spectra integrates several metric spaces and answers queries based on one or more descriptors at once. Moreover, Spectra relies on existing correlations among different spaces to choose the best descriptors to obtain a concise yet accurate indexing space. Thus, it reduces the number of distance calculations, speeding up the query execution, and improving the resulting quality.