12,266 research outputs found
Fine-grained classification of low-resolution image
Successful fine-grained image classification methods learn subtle details between visually similar (sub-)categories, but the problem becomes significantly more challenging if the details are missing due to low resolution. Alternatively, encouraged by the recent success of Fully Convolutional Neural Network (FCNN) architectures in single image super-resolution, we propose a novel Resolution-Aware Classification Neural Network (RACNN). More precisely, we combine convolutional image super-resolution and convolutional fine-grained classification together in an end-to-end cascade manner, which first improves the resolution of low-resolution images and then recognises objects in the images. Extensive experiments on the Stanford Cars, Caltech-UCSD Birds 200-2011 and Oxford 102 Category Flowers benchmarks demonstrate that the proposed model consistently performs better than conventional convolutional models on categorising fine-grained object classes in low-resolution images
Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery
Fine-grained object recognition that aims to identify the type of an object
among a large number of subcategories is an emerging application with the
increasing resolution that exposes new details in image data. Traditional fully
supervised algorithms fail to handle this problem where there is low
between-class variance and high within-class variance for the classes of
interest with small sample sizes. We study an even more extreme scenario named
zero-shot learning (ZSL) in which no training example exists for some of the
classes. ZSL aims to build a recognition model for new unseen categories by
relating them to seen classes that were previously learned. We establish this
relation by learning a compatibility function between image features extracted
via a convolutional neural network and auxiliary information that describes the
semantics of the classes of interest by using training samples from the seen
classes. Then, we show how knowledge transfer can be performed for the unseen
classes by maximizing this function during inference. We introduce a new data
set that contains 40 different types of street trees in 1-ft spatial resolution
aerial data, and evaluate the performance of this model with manually annotated
attributes, a natural language model, and a scientific taxonomy as auxiliary
information. The experiments show that the proposed model achieves 14.3%
recognition accuracy for the classes with no training examples, which is
significantly better than a random guess accuracy of 6.3% for 16 test classes,
and three other ZSL algorithms.Comment: G. Sumbul, R. G. Cinbis, S. Aksoy, "Fine-Grained Object Recognition
and Zero-Shot Learning in Remote Sensing Imagery", IEEE Transactions on
Geoscience and Remote Sensing (TGRS), in press, 201
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