Image Description Generator using Residual Neural Network and Long Short-Term Memory

Mahesh Kumar Morampudi, Nagamani Gonthina, Nuthanakanti Bhaskar, V. Reddy
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Abstract

Human beings can describe scenarios and objects in a picture through vision easily whereas performing the same task with a computer is a complicated one. Generating captions for the objects of an image helps everyone to understand the scenario of the image in a better way. Instinctively describing the content of an image requires the apprehension of computer vision as well as natural language processing. This task has gained huge popularity in the field of technology and there is a lot of research work being carried out. Recent works have been successful in identifying objects in the image but are facing many challenges in generating captions to the given image accurately by understanding the scenario. To address this challenge, we propose a model to generate the caption for an image. Residual Neural Network (ResNet) is used to extract the features from an image. These features are converted into a vector of size 2048. The caption generation for the image is obtained with Long Short-Term Memory (LSTM). The proposed model is experimented on the Flickr8K dataset and obtained an accuracy of 88.4\%. The experimental results indicate that our model produces appropriate captions compared to the state of art models.
基于残差神经网络和长短期记忆的图像描述生成器
人类可以很容易地通过视觉来描述图片中的场景和物体,而用计算机来完成同样的任务是一件很复杂的事情。为图像对象生成标题有助于每个人以更好的方式理解图像的场景。本能地描述图像的内容需要计算机视觉和自然语言处理的理解。这项任务在技术领域获得了巨大的普及,并开展了大量的研究工作。最近的工作已经成功地识别了图像中的物体,但在通过理解场景准确地为给定图像生成字幕方面面临许多挑战。为了解决这个问题,我们提出了一个模型来生成图像的标题。残差神经网络(ResNet)用于提取图像的特征。这些特征被转换成大小为2048的向量。利用长短期记忆(LSTM)方法生成图像的标题。该模型在Flickr8K数据集上进行了实验,获得了88.4%的准确率。实验结果表明,与现有的模型相比,我们的模型产生了合适的标题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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