How Can I Index My Thousands of Photos Effectively and Automatically? An Unsupervised Feature Selection Approach

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Book Chapter


Given a large photo collection without domain knowledge (e.g., tourism photos, conference photos, event photos, images wrapped from webpages), it is not easy for human beings to organize or only view them within a reasonable time. In this paper, we propose to automatically extract meaningful semantics from a photo collection named “dimensions” to help people view, search and organize photos conveniently and efficiently. However, due to the lack of additional domain knowledge or content information, existing image retrieval techniques are not applicable. To tackle the problem, we first propose a simple strategy to extract all meaningful semantics from original photos/images as candidate dimensions, and then propose an efficient unsupervised feature/dimension selection method to select a sufficient dimension subset to uniquely index each photo within this collection. Our experiments on several real-world photo/image collections validate both the efficiency and effectiveness of our proposed method.

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Proceedings of the 2014 SIAM International Conference on Data Mining

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