On the Relationship Between Color and Texture Features and Their Use in Shape Classification


On the Relationship Between Color and Texture Features and Their Use in Shape Classification – We propose a new framework for the purpose of image annotation using multinomial random processes (NNPs). NNs encode the information contained in a set of image samples and the data are modelled as either the image samples and their distributions, or the images. In this framework, we treat the data from different samples as the same. NNs are built from multiple distributions and these are represented as a set of random Gaussian processes (GRPs). This allows the proposed framework to cope with multi-view learning problems. In this paper, the proposed framework is compared with an existing framework on two problems: the classification of image-level shapes and the classification of texture features. The experimental results demonstrate that the framework is robust and provides an alternative approach to image annotation.

Visual attention is being used to improve the quality of a person’s visual experience, but the underlying mechanisms are still under investigation. In this work, attention is employed to predict the next person’s gaze. Such a model is used to predict the next person’s gaze, which is a natural and meaningful information in human visual perception. Our model was trained for object detection through face recognition. In this work, trained in an attention-based fashion, we used a Convolutional Neural Network (CNN). Our algorithm trained to predict the next person’s gaze can be implemented by the proposed deep attention model. Results suggest that deep attention can help a person’s visual sense of depth and attention.

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On the Relationship Between Color and Texture Features and Their Use in Shape Classification

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    DeepFace2Face: A Fully Convolutional Neural Network for Real-Time Face RecognitionVisual attention is being used to improve the quality of a person’s visual experience, but the underlying mechanisms are still under investigation. In this work, attention is employed to predict the next person’s gaze. Such a model is used to predict the next person’s gaze, which is a natural and meaningful information in human visual perception. Our model was trained for object detection through face recognition. In this work, trained in an attention-based fashion, we used a Convolutional Neural Network (CNN). Our algorithm trained to predict the next person’s gaze can be implemented by the proposed deep attention model. Results suggest that deep attention can help a person’s visual sense of depth and attention.


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