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WEAKLY SUPERVISED DEEP CONVOLUTIONAL NETWORKS FOR FINE-GRAINED OBJECT RECOGNITION IN MULTISPECTRAL IMAGES
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Date
2019-01-01
Author
Aygunes, Bulut
AKSOY, SELİM
Cinbiş, Ramazan Gökberk
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The challenging task of training object detectors for fine-grained classification faces additional difficulties when there are registration errors between the image data and the ground truth. We propose a weakly supervised learning methodology for the classification of 40 types of trees by using fixed-sized multispectral images with a class label but with no exact knowledge of the object location. Our approach consists of an end-to-end trainable convolutional neural network with separate branches for learning class-specific and location-specific scoring of image regions. Comparative experiments show that the proposed method simultaneously learns to detect and classify the objects of interest with high accuracy.
Subject Keywords
Weakly Supervised Learning
,
Object Recognition
,
Multispectral Image Analysis
URI
https://hdl.handle.net/11511/39730
DOI
https://doi.org/10.1109/igarss.2019.8899170
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Department of Computer Engineering, Conference / Seminar
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B. Aygunes, S. AKSOY, and R. G. Cinbiş, “WEAKLY SUPERVISED DEEP CONVOLUTIONAL NETWORKS FOR FINE-GRAINED OBJECT RECOGNITION IN MULTISPECTRAL IMAGES,” presented at the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, JAPAN, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/39730.