基于CNN的智能时尚对象分类

Debabrata Swain, Kaxit Pandya, Jay Sanghvi, Yugandhar Manchala
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引用次数: 0

摘要

全世界视力受损的人数每年都在急剧增加。目前,约有22亿人患有视力障碍。我们的模式将影响公众生活的主要领域之一是为有特殊能力人士提供家居协助的领域。由于视力的改善,这些人面临着许多问题。因此,对于这群人来说,在物体识别方面对辅助系统有很高的需求。对于有特殊能力的人来说,有时很难区分与服装相关的物品,因为它们非常相似。为了更好地进行对象分类,我们使用了一个包含计算机视觉和CNN的模型。计算机视觉是人工智能的一个领域,它帮助识别视觉对象。在这里,基于cnn的模型被用于更好地分类服装和时尚物品。另一种被称为Lenet的模型被使用,它具有更强的体系结构。Lenet是一种多层卷积神经网络,主要用于图像分类任务。模型构建和验证使用MNIST时尚数据集。
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An Intelligent Fashion Object Classification Using CNN
Every year the count of visually impaired people is increasing drastically around the world. At present time, approximately 2.2 billion people are suffering from visual impairment. One of the major areas where our model will affect public life is the area of house assistance for specially-abled persons. Because of visual improvement, these people face lots of issues. Hence for this group of people, there is a high need for an assistance system in terms of object recognition. For specially-abled people sometimes it becomes really difficult to identify clothing-related items from one another because of high similarity. For better object classification we use a model which includes computer vision and CNN. Computer vision is the area of AI that helps to identify visual objects. Here a CNN-based model is used for better classification of clothing and fashion items. Another model known as Lenet is used which has a stronger architectural structure. Lenet is a multi-layer convolution neural network that is mainly used for image classification tasks. For model building and validation MNIST fashion dataset is used.
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CiteScore
4.00
自引率
0.00%
发文量
15
审稿时长
10 weeks
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