A DISTINCTIVE ARCHITECTURE TO INITIATE PICTURE SLOGAN WITH INTELLECTUAL STRATEGIES

Abstract

This study proposes an image description producer combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to automatically produce textual metaphors for input imageries. The model extracts high-level features from images and feeds them into LSTM networks for coherent and contextually relevant captions. The architecture involves pre-trained CNNs for feature extraction, caption preprocessing to tokenize and prepare text data, and a combined CNN-LSTM model for training and inference. The dataset used for training consists of image-caption pairs, where CNN extracts meaningful visual features and feeds them into the LSTM. The LSTM learns sequential dependencies in captions and generates coherent textual descriptions. A decoder mechanism handles the generation process, allowing flexibility in output length. The model is trained using loss functions like categorical cross-entropy and sequence-to-sequence loss to optimize the generation process and encourage caption diversity. Experimental results show that the CNN-LSTM-based image caption generator achieves competitive performance, generating descriptive and contextually relevant captions. The model finds potential applications in domains like image annotation, assisting visually impaired users, and enhancing content understanding in image-based search engines. Overall, the combination of CNN and LSTM is a robust and effective solution for generating descriptive captions from images, showcasing the continuous advancement of deep learning techniques in computer vision and natural language processing.