• Syed Qamrul Kazmi
• Munindra Kumar Singh
• Saurabh Pal

Content-Based Image Retrieval (CBIR) is a technology and methodology used in computer vision and information retrieval to retrieve images from databases based on their visual content rather than metadata or text-based descriptions. Images are indexed and retrieved in CBIR systems utilizing their intrinsic visual qualities such as color, texture, shape, and other visual characteristics. Art galleries, the fashion business, medical imaging, satellite image analysis, and other industries use CBIR. Designing effective and efficient feature extraction approaches that capture the most significant parts of the visual content while minimizing computing cost is one of the problems in CBIR. Furthermore, finding the best similarity measuring metric is critical for accurate retrieval results. In this paper we have used Decision Tree Classifier and particle swarm optimization for creating a decision tree model to classify images based on their visual qualities which is the first step in using a decision tree for content-based image retrieval (CBIR). Particle Swarm Optimization is inspired by the social behavior of birds and fish. It involves a population of particles that move through a search space to find optimal solutions. Each particle adjusts its position based on its own experience and the experiences of other particles.

Decision Tree Classifier, Content based image retrieval, Particle Swarm Optimization, Data mining

"An Efficient Content Based Image Retrieval System Using Decision Tree Classifier and Particle Swarm Optimization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 8, page no.f565-f572, August-2023, Available :http://www.jetir.org/papers/JETIR2308566.pdf

"An Efficient Content Based Image Retrieval System Using Decision Tree Classifier and Particle Swarm Optimization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 8, page no. ppf565-f572, August-2023, Available at : http://www.jetir.org/papers/JETIR2308566.pdf