In the wild image retrieval and clustering for 3D cultural heritage landmarks reconstruction

One of the main characteristics of Internet era is the free and online availability of extremely large collections of images located on distributed and heterogeneous platforms over the web. The proliferation of millions of shared photographs spurred the emergence of new image retrieval techniques based not only on images’ visual information, but on geo-location tags and camera exif data. These huge visual collections provide a unique opportunity for cultural heritage documentation and 3D reconstruction. The main difficulty, however, is that the internet image datasets are unstructured containing many outliers. For this reason, in this paper a new content-based image filtering is proposed to discard image outliers that either confuse or significantly delay the followed e-documentation tools, such as 3D reconstruction of a cultural heritage object. The presented approach exploits and fuses two unsupervised clustering techniques: DBSCAN and spectral clustering. DBSCAN algorithm is used to remove outliers from the initially retrieved dataset and spectral clustering discriminate the noise free image dataset into different categories each representing characteristic geometric views of cultural heritage objects. To discard the image outliers, we consider images as points onto a multi-dimensional manifold and the multi-dimensional scaling algorithm is adopted to relate the space of the image distances with the space of Gram matrices through which we are able to compute the image coordinates. Finally, structure from motion is utilized for 3D reconstruction of cultural heritage landmarks. Evaluation on a dataset of about 31,000 cultural heritage images being retrieved from internet collections with many outliers indicate the robustness and cost effectiveness of the proposed method towards a reliable and just-in-time 3D reconstruction than existing state-of-the-art techniques.