We propose a fusion neural network classifier which divides each descriptor by the number of its input features.
Neural networks have been commonly used for image classification problems by fusing input features extracted from multiple MPEG-7 descriptors.
In order to solve the problem, we propose a fusion neural network classifier which divides each descriptor by the number of its input features. And we consider ...
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A fusion neural network classifier for image classification
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A robust hybrid combination technique to build a combined classifier that is able to tackle the problem of classification of rotation-invariant 2D textures ...
The document describes a fusion neural network classifier that is proposed to improve image classification performance. The classifier divides the input ...
In order to solve the problem, we propose a fusion neural network classifier which divides each descriptor by the number of its input features. And we consider ...
The project addresses the challenge of enhancing image classification accuracy by employing a fusion approach that combines the strengths of image generation ...
This framework includes two parts: data processing and image classification. ... Feature extraction subnetworks extract the shallow features of the image and ...
In this paper, we propose a deep neural network framework named Fusion-Net for image classification. Our contributions include: (1) We implement dimension ...
Specifically, MCN can directly make full use of features of different levels and fuse intermediate results in a self-adaption way. Note that the auxiliary ...