Published Paper Details:

The proliferation of digital cameras, social media platforms, and online image-sharing services has led to an enormous amount of images being created and shared every day. This explosion in the volume of images presents both opportunities and challenges, particularly in the field of content-based image retrieval (CBIR) and image management. We can have rich visual content, data input for various image related tasks and creating innovative applications. By applying cutting-edge machine learning and deep learning techniques as well as investigating cutting-edge indexing and retrieval strategies, researchers and developers are always attempting to improve CBIR algorithms. Large image files may now be stored and processed more effectively as an introduction of distributed storage and cloud computing technologies. The industry is developing quickly to take advantage of the potential provided by the abundance of visual content that is already available. This approach makes use of an image's size, texture, and dominating colors. The analysis of image data is the focus of the emerging data mining subfield known as image mining. Images can be retrieved from a vast dataset using a variety of techniques. However, they have some shortcomings. In order to extract data from images, this research uses image mining techniques including clustering and associations rules mining. Additionally, it combines multimodal elements including visual and textual components