During the last years, the problem of landmark recognition is addressed in many different ways. Landmark recognition is related to finding the most similar images to a starting one in a particular dataset of buildings or places. This chapter explains the most used techniques for solving the problem of landmark recognition, with a specific focus on techniques based on deep learning. Firstly, the focus is on the classical approaches for the creation of descriptors used in the content-based image retrieval task. Secondly, the deep learning approach that has shown overwhelming improvements in many tasks of computer vision, is presented. A particular attention is put on the major recent breakthroughs in Content-Based Image Retrieval (CBIR), the first one is transfer learning which improves the feature representation and therefore accuracy of the retrieval system. The second one is the fine-tuning technique, that allows to highly improve the performance of the retrieval system, is presented. Finally, the chapter exposes the techniques for large-scale retrieval, in which datasets contain at least a million images.

Landmark Recognition: From Small-Scale to Large-Scale Retrieval / Magliani, Federico; Fontanini, Tomaso; Prati, Andrea. - STAMPA. - (2019), pp. 237-259. [10.1007/978-3-030-03000-1_10]

Landmark Recognition: From Small-Scale to Large-Scale Retrieval

Federico Magliani
Methodology
;
Tomaso Fontanini
Writing – Original Draft Preparation
;
Andrea Prati
Writing – Review & Editing
2019

Abstract

During the last years, the problem of landmark recognition is addressed in many different ways. Landmark recognition is related to finding the most similar images to a starting one in a particular dataset of buildings or places. This chapter explains the most used techniques for solving the problem of landmark recognition, with a specific focus on techniques based on deep learning. Firstly, the focus is on the classical approaches for the creation of descriptors used in the content-based image retrieval task. Secondly, the deep learning approach that has shown overwhelming improvements in many tasks of computer vision, is presented. A particular attention is put on the major recent breakthroughs in Content-Based Image Retrieval (CBIR), the first one is transfer learning which improves the feature representation and therefore accuracy of the retrieval system. The second one is the fine-tuning technique, that allows to highly improve the performance of the retrieval system, is presented. Finally, the chapter exposes the techniques for large-scale retrieval, in which datasets contain at least a million images.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2849702
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