Image Caption Generator Using Resnet50 and Lstm
Kamalam, Suganya Baby, Naveenkumar, Subiga, Yadhuvarshini · 2024
Image captioning is an advanced technique used to generate textual descriptions that can describe the content and context of an image. This process involves both NLP and computer vision to automatically generating detailed description of image that explain the content visually depicted in an image. These descriptions are valuable in many domains, providing insights into image content and aiding in a deeper understanding of visual data. Image captioning is applicable in various fields, including analyzing large collections of unlabeled images. By automatically generating descriptions, image captioning helps extract meaningful information and identify patterns in these images. In machine learning, image captioning is useful for guiding autonomous systems like self-driving cars. By providing contextual descriptions of the environment captured by cameras, image captioning helps in navigation and decision-making. Additionally, image captioning enhances accessibility for visually impaired individuals by providing narrations that enable them to understand visual content and interact with digital media effectively. The implementation of image captioning typically involves deep learning models. A ConvolutionalNeuralNetwork (CNN) serves as the encoder, extracting relevant features from input images. Architectures like ResNet50 (Residual Network) are commonly used for their effectiveness in extracting hierarchical features from images. The decoder, on the other hand, uses the LongShort-TermMemory (LSTM) variant, to generate textual descriptions. The LSTM architecture is well-suited for processing sequential data, making it effective in generating coherent and contextually relevant captions. Overall, the integration of neural network models for image captioning represents a significant advancement in computer vision and NLP. By harnessing the power of deep learning image captioning systems can accurately describe visual content, leading to enhanced understanding and utilization of images across various domains and applications.