基于卷积神经网络的视频分类检索

发布时间：2018-01-03 11:05

本文关键词：基于卷积神经网络的视频分类检索　出处：《浙江大学》2017年硕士论文　论文类型：学位论文

【摘要】：随着深度学习在图像识别领域的巨大成功,深度学习被应用到越来越多的领域,包括视频处理领域,文本处理领域以及音频处理领域。卷积神经网络是深度学习中非常重要的一类网络模型,它和传统神经网络的区别在于引入了卷积层、池化层。卷积神经网络在图像识别领域取得成功之后,研究人员开始逐步将其应用于视频分类任务中,并取得了分类效果上的提高,这充分说明了卷积神经网络在视频分类任务中起到的重要作用。本文的研究内容在于实现一个通用且有效的分类检索模型。在图像研究领域有研究人员提出在卷积神经网络中引入哈希层来得到图像对应的0-1向量哈希码,使用这个哈希码来进行相似图片的查找,取得了不错的效果。本文的一个创新点在于将这种思路引入到了视频处理领域,.验证了哈希层在这个应用场景中的有效性,继而提出通用且效果更好的分类检索模型;另外一个创新点在于采用了更为高效的检索算法来提高模型的可用性。本文在调研了相关领域的工作之后,采用了基于VGG-NET神经网络模型的视频分类模型,在模型中引入哈希层来得到视频对应的0-1向量哈希码。在模型对视频数据分类功能方面,通过实验验证了分类模型的准确率;在得到哈希码后的模型检索部分,本文提出的改进的检索算法和朴素的依次比较算法相比在检索时间上有显著地改进。总的来说,本文实现的通用分类检索模型具有更好的分类准确率和更快的检索速度。
[Abstract]:With the great success of depth learning in the field of image recognition, depth learning has been applied to more and more fields, including video processing. In the field of text processing and audio processing, convolution neural network is a very important network model in depth learning. The difference between it and traditional neural network is the introduction of convolution layer. After the success of convolution neural network in the field of image recognition, researchers began to gradually apply it to the task of video classification, and achieved improved classification effect. This fully illustrates the important role of convolutional neural network in video classification task. The research content of this paper is to implement a general and effective classification and retrieval model. In the field of image research, some researchers have put forward in the field of volume. A hash layer is introduced into the product neural network to obtain the 0-1 vector hash code corresponding to the image. Using this hash code to find similar images, good results have been achieved. One of the innovations of this paper is to introduce this idea into the field of video processing. The effectiveness of hash layer in this application scenario is verified, and then a generic and better classification retrieval model is proposed. Another innovation is the use of more efficient retrieval algorithms to improve the availability of the model. The video classification model based on VGG-NET neural network model is adopted, and a hash layer is introduced into the model to get the corresponding 0-1 vector hash code. The accuracy of the classification model is verified by experiments. In the part of model retrieval after hashing code, the improved retrieval algorithm and the simple sequential comparison algorithm proposed in this paper have a significant improvement in retrieval time. The general classification retrieval model realized in this paper has better classification accuracy and faster retrieval speed.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41;TP183

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