IEEE Transactions on Image Processing

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Image annotation aims to annotate a given image with a variable number of class labels corresponding to diverse visual concepts. In this paper, we address two main issues in large-scale image annotation: 1) how to learn a rich feature representation suitable for predicting a diverse set of visual concepts ranging from object, scene to abstract concept and 2) how to annotate an image with the optimal number of class labels.

Zero-shot learning (ZSL) for visual recognition aims to accurately recognize the objects of unseen classes through mapping the visual feature to an embedding space spanned by class semantic information. However, the semantic gap across visual features and their underlying semantics is still a big obstacle in ZSL. Conventional ZSL methods construct that the mapping typically focus on the original visual features that are independent of the ZSL tasks, thus degrading the prediction performance.

The IEEE Transactions on Image Processing covers novel theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications.

Gaurav SharmaEditor-in-Chief:
Gaurav Sharma
University of Rochester, USA
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Term Ends: 31 December 2020

 

 

 

NAME DESCRIPTION
Image & Video Sensing, Modeling, and Representation
SMR-SEN Image & Video Sensing and Acquisition
Scanning, sampling and quantization; Sensor systems and distributed sensing; Video stabilization and autofocus; Intrinsic and extrinsic camera model estimation; Coded aperture systems; Omnidirectional imaging and plenoptics
SMR-SMD

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