论文标题
社交媒体图像的设备过滤以有效存储
On-device Filtering of Social Media Images for Efficient Storage
论文作者
论文摘要
人为制作的图像，例如模因，季节性问候等，正在淹没当今社交媒体平台。这些最终开始占据智能手机的大量内部内存，用户可以浏览数百张图像并删除这些合成图像，这会变得笨拙。为了解决这个问题，我们提出了一种基于卷积神经网络（CNN）的新方法，以通过对这些综合图像进行分类并允许用户一口气将其删除，以实现社交媒体图像的设备过滤。自定义模型使用深度可分离的卷积层来实现智能手机上的推理时间较低。我们已经对各种相机图像数据集进行了大量评估，以涵盖相机捕获的图像的大多数方面。还测试了各种综合社交媒体图像。所提出的解决方案在ploce-365数据集上的准确度为98.25％，在包含30K实例的合成图像数据集上，该解决方案的准确性为95.81％。
Artificially crafted images such as memes, seasonal greetings, etc are flooding the social media platforms today. These eventually start occupying a lot of internal memory of smartphones and it gets cumbersome for the user to go through hundreds of images and delete these synthetic images. To address this, we propose a novel method based on Convolutional Neural Networks (CNNs) for the on-device filtering of social media images by classifying these synthetic images and allowing the user to delete them in one go. The custom model uses depthwise separable convolution layers to achieve low inference time on smartphones. We have done an extensive evaluation of our model on various camera image datasets to cover most aspects of images captured by a camera. Various sorts of synthetic social media images have also been tested. The proposed solution achieves an accuracy of 98.25% on the Places-365 dataset and 95.81% on the Synthetic image dataset that we have prepared containing 30K instances.
