Abstract:

Automatic image caption generation is one of the frequent goals of computer vision. Image description generation models must solve a larger number of complex problems to have this task successfully solved. The objects in the image must be detected and recognized, after which a logical and syntactically correct textual description is generated. For that reason, description generation is a complex problem. It is an extremely important challenge for machine learning algorithms because it represents an impersonation of a complicated human ability to encapsulate huge amounts of highlighted visual pieces of information in descriptive language. As the deep learning techniques are growing, huge datasets and computer power are helpful to build models that can generate captions for an image. Hence in this work, an automatic image caption generation approach using LSTM and CNN is presented. In this project, deep learning techniques like CNN (Convolutional Neural Network) and LSTM (Long Short Term Memory) are used to identify the caption of the image. Image caption generator is a process which involves natural language processing and computer vision concepts to recognize the context of an image and present it in English. In this project, some of the core concepts of image captioning and its common approaches are followed. Keras library, numpy and jupyter notebooks are used for making of this project. This project also discusses about flickr_dataset and CNN used for image classification.

Description:

Automatic Image Caption Generation, Deep Learning (DL), Convolutional Neural Network) and Long Short Term Memory (LSTM).