Lisbon Landmark Lenslet Light Field Dataset: Description and Retrieval Performance

Popular local feature extraction schemes, such as SIFT, are robust when changes in illumination, translation and scale occur, and play an important role in visual content retrieval. However, these solutions are not very robust to 3D object rotations and camera viewpoint changes. In such scenarios, the emerging and richer lenslet light field image representation can provide additional information such as multiple perspectives and depth data. This paper introduces a new lenslet light field imaging dataset and studies the retrieval performance when popular 2D visual descriptors are applied. The new dataset consists of 25 Lisbon landmarks captured with a lenslet camera from different perspectives. Moreover, this paper proposes and assesses straightforward extensions of visual 2D descriptor matching for lenslet light field retrieval. The experimental results show that gains up to 14% can be obtained with a light field representation when compared to a 2D imaging conventional representation.