Image Captioning with Deep learning: Every day, we see a large number of images from various sources on the Internet, news articles and advertisement etc. Probably, most of the images would have their captions below of images. With the captions it would be easy to understand. Sometimes, the viewers need to understand themselves if there are no captions for it. Most images have no description, but human beings can understand most of them without their detailed captions.
In short, image captioning is the process to automatically generate the human like descriptions of the images. If humans need automatic image captioning technique, then machine needs to understand those images. That’s the reason for researchers, to began working on object identification in images.
It has become clear that it is not as good to provide only the names of identified objects. With just identification of objects, the machine cannot give the captions just like humans captioning.
As long as machines do not think, speak and behave like human beings, natural language explanations may become a challenge to solve. To overcome this challenge, image captioning using deep learning is evolved for generating the description of an images as a result.
Additionally, Object detection and image classification task is needed to identify objects within the image. Moreover, identifying the objects, identifying the relationship between them and total scene of the image are important.
After understanding the scene, it is required to generate a human like description of that image. This description generating process is a machine learning task that involves both natural language processing(for text generation)and computer vision(for understanding image contents). It is very dominant task with good practical and industrial significance.
In the last few years, deep learning made a huge success in the field of computer vision.
The software requirements are as follows:
- Language Used: Python – 3.8.5
- Libraries: NumPy and NLTK
- Frameworks: Keras – 2.4.3 and TensorFlow – 2.3.0
- IDE: Jupyter Notebook and Spyder Editor
The Hardware Requirements are as follows:
- Operating System: Windows-10
- Memory : 8 GB RAM
We can develop image captioning model with many datasets. Convolutional Neural Networks (CNN) in existing system extracts the image features. Recurrent Neural Networks (RNN) takes responsibility of generating captions.
Reference for developing a deep learning photo caption generator from scratch:https://machinelearningmastery.com/develop-a-deep-learning-caption-generation-model-in-python/
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