Results 11 to 20 of about 257,647 (295)
Hyperspectral image classification via contextual deep learning [PDF]
Eurasip Journal on Image and Video Processing, 2015 AbstractBecause the reliability of feature for every pixel determines the accuracy of classification, it is important to design a specialized feature mining algorithm for hyperspectral image classification. We propose a feature learning algorithm, contextual deep learning, which is extremely effective for hyperspectral image classification.
Xiaorui Ma, Jie Geng, Hongyu Wang
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Contextual classification of multispectral image data [PDF]
Pattern Recognition, 1981 Abstract Compound decision theory is invoked to develop a method for classifying image data using spatial context. Methods for characterizing contextual information in an image are proposed and tested. Experimental results based on both simulated and real multispectral remote sensing data demonstrate the effectiveness of the contextual classifier.
Stephen B Vardeman, James C Tilton
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Going Deeper With Contextual CNN for Hyperspectral Image Classification [PDF]
IEEE Transactions on Image Processing, 2017 14 ...
Hyungtae Lee, Heesung Kwon
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ciu.image: An R Package for Explaining Image Classification with Contextual Importance and Utility [PDF]
Lecture Notes in Computer Science, 2021 Many techniques have been proposed in recent years that attempt to explain results of image classifiers, notably for the case when the classifier is a deep neural network. This paper presents an implementation of the Contextual Importance and Utility method for explaining image classifications.
Kary Främling +2 more
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Image Classification with Rejection using Contextual Information [PDF]
CoRR, 2015 We introduce a new supervised algorithm for image classification with rejection using multiscale contextual information. Rejection is desired in image-classification applications that require a robust classifier but not the classification of the entire image.
Filipe Condessa +4 more
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Automatic joint segmentation and classification of breast ultrasound images via multi-task learning with object contextual attention [PDF]
Frontiers in OncologyThe segmentation and classification of breast ultrasound (BUS) images are crucial for the early diagnosis of breast cancer and remain a key focus in BUS image processing.
Yaling Lu +3 more
doaj +2 more sources
MathematicsMulti-label image classification (MLIC) is vulnerable to contextual bias, where models may exploit spurious label–context associations rather than object evidence, leading to degraded generalization under distribution shifts.
Baiqing Liu +4 more
doaj +2 more sources
Contextual superpixel description for remote sensing image classification [PDF]
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015 The performance of pattern classifiers depends on the separability of the classes in the feature space — a property related to the quality of the descriptors — and the choice of informative training samples for user labeling — a procedure that usually requires active learning.
John E. Vargas +5 more
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From ImageNet to Image Classification: Contextualizing Progress on Benchmarks [PDF]
CoRR, 2020 Building rich machine learning datasets in a scalable manner often necessitates a crowd-sourced data collection pipeline. In this work, we use human studies to investigate the consequences of employing such a pipeline, focusing on the popular ImageNet dataset.
Dimitris Tsipras +4 more
+7 more sources
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification [PDF]
Advances in Neural Information Processing Systems 35, 2022 Recent years have seen a growth in user-centric applications that require effective knowledge transfer across tasks in the low-data regime. An example is personalization, where a pretrained system is adapted by learning on small amounts of labeled data belonging to a specific user. This setting requires high accuracy under low computational complexity,
Massimiliano Patacchiola +5 more
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