Publicación: CROSS-SPECTRAL LOCAL DESCRIPTORS VIA QUADRUPLET NETWORK
Fecha
2017
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THIS PAPER PRESENTS A NOVEL CNN-BASED ARCHITECTURE, REFERRED TO AS Q-NET, TO LEARN LOCAL FEATURE DESCRIPTORS THAT ARE USEFUL FOR MATCHING IMAGE PATCHES FROM TWO DIFFERENT SPECTRAL BANDS. GIVEN CORRECTLY MATCHED AND NON-MATCHING CROSS-SPECTRAL IMAGE PAIRS, A QUADRUPLET NETWORK IS TRAINED TO MAP INPUT IMAGE PATCHES TO A COMMON EUCLIDEAN SPACE, REGARDLESS OF THE INPUT SPECTRAL BAND. OUR APPROACH IS INSPIRED BY THE RECENT SUCCESS OF TRIPLET NETWORKS IN THE VISIBLE SPECTRUM, BUT ADAPTED FOR CROSS-SPECTRAL SCENARIOS, WHERE, FOR EACH MATCHING PAIR, THERE ARE ALWAYS TWO POSSIBLE NON-MATCHING PATCHES: ONE FOR EACH SPECTRUM. EXPERIMENTAL EVALUATIONS ON A PUBLIC CROSS-SPECTRAL VIS-NIR DATASET SHOWS THAT THE PROPOSED APPROACH IMPROVES THE STATE-OF-THE-ART. MOREOVER, THE PROPOSED TECHNIQUE CAN ALSO BE USED IN MONO-SPECTRAL SETTINGS, OBTAINING A SIMILAR PERFORMANCE TO TRIPLET NETWORK DESCRIPTORS, BUT REQUIRING LESS TRAINING DATA.
