A non-parametric unsupervised approach for content based image retrieval and clustering

Abstract

Nowadays, there are available extremely large collections of images located on distributed and heterogeneous platforms over the web. The proliferation of billions of shared photos has outpaced the current technology for browsing such collections, but at the same time it spurred the emergence of new image retrieval techniques based not only on photos' visual information, but on geo-location tags and camera exif data. Although, additional image information may be proven very useful for preliminary image retrieval, the final retrieved result is necessary to be refined by exploiting visual information. In this paper we present a process for refining image retrieval results by exploiting and fusing two unsupervised clustering techniques: DBSCAN and spectral clustering. DBSCAN algorithm is used to remove outliers from the initially retrieved image set, and spectral clustering finalizes retrieval process by clustering together visually similar images. However, DBSCAN and spectral clustering require manual tunning of their parameters, which usually requires a priori knowledge of the dataset. To overcome this problem we developed a tuning mechanism that automatically tunes the parameters of both algorithms. For the evaluation of the proposed approach we used thousands of images from Flickr downloaded using text queries for well known cultural heritage monuments.