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Image Segmentation using Convolutional Neural Network for Image Annotation

机译:使用卷积神经网络进行图像标注的图像分割

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摘要

Due to rapid advancement in the digital display, communication and storage devices with effective techniques are needed to organize, index, retrieve and annotate a large image database. Image segmentation finds application in various areas of image processing and computer vision. Inferring of low level features from the given image is a challenging task in unstructured regions. Most of the annotation and retrieval algorithms fail to consider region semantics. This paper proposes convolutional neural network (CNN) based image segmentation for image annotation application. The proposed CNN includes pixel based prediction of the regions that are applied to obtain low level image features. The algorithm uses image region information based on the precise color distribution within the image. Experimental results demonstrate better results of image segmentation using CNN.
机译:由于数字显示器的快速发展,需要具有有效技术的通信和存储设备来组织,索引,检索和注释大型图像数据库。图像分割可应用于图像处理和计算机视觉的各个领域。在非结构化区域中,从给定图像推断低级特征是一项艰巨的任务。大多数注释和检索算法都没有考虑区域语义。本文提出了基于卷积神经网络(CNN)的图像分割方法,用于图像标注。所提出的CNN包括被应用于获得低级图像特征的区域的基于像素的预测。该算法基于图像内的精确颜色分布使用图像区域信息。实验结果证明了使用CNN进行图像分割的更好结果。

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