  The growing volume of digital images necessitates advanced systems for
efficient categorization and retrieval, presenting a significant challenge in
database management and information retrieval. This paper introduces PICS
(Pipeline for Image Captioning and Search), a novel approach designed to
address the complexities inherent in organizing large-scale image repositories.
PICS leverages the advancements in Large Language Models (LLMs) to automate the
process of image captioning, offering a solution that transcends traditional
manual annotation methods. The approach is rooted in the understanding that
meaningful, AI-generated captions can significantly enhance the searchability
and accessibility of images in large databases. By integrating sentiment
analysis into the pipeline, PICS further enriches the metadata, enabling
nuanced searches that extend beyond basic descriptors. This methodology not
only simplifies the task of managing vast image collections but also sets a new
precedent for accuracy and efficiency in image retrieval. The significance of
PICS lies in its potential to transform image database systems, harnessing the
power of machine learning and natural language processing to meet the demands
of modern digital asset management.
