![]() Neural Comput 9(8):1735–1780īai S, An S (2018) A survey on automatic image caption generation. Hochreiter S, Schmidhuber J (1997) Long short-term memory. ![]() In: 2017 international conference on engineering and technology (ICET). In: Proceedings of the IEEE conference on computer vision and pattern recognitionĪlbawi S, Tareq AM, Saad A-Z (2017) Understanding of a convolutional neural network. Vinyals O et al (2015) Show and tell: a neural image caption generator. The system is trained for 70 epochs with a batch size of 512 the proposed system achieves a performance score of 0.568. The system is evaluated with BLEU scores to evaluate the model’s efficiency. This model is trained so that if the input image is given to the model, it will generate captions or sentences describing the image. LSTM works as a decoder to generate sentences or captions for the images. The pre-trained VGG16 is used to extract features from the given image. This model was developed to build an image caption generator by implementing the convolutional neural network with long short-term memory. We use the LSTM model to generate text or sentences or captions for the given input images. The system takes the pre-trained deep learning convolutional neural network (CNN) architecture VGG16 model for learning the image features, uses long short-term memory (LSTM) for learning the text features, and combines the image’s result with an LSTM to generate a caption for the image. This paper proposes an image caption generator that will accept an image as an input and generate an English sentence as output by labeling the image’s content using two optimization techniques such as beam search and greedy search. The main challenge of the system is to generate correct captions for the given image. The image caption generator’s main aim is to generate an English sentence or caption for an image automatically. ![]()
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