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@article{178602,
        author = {Ms Megha Jogiya and Ms. Rauki Yadav},
        title = {An Optimized Image Retrieval using Clustering And Classification Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4099-4102},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178602},
        abstract = {Content Based Image Retrieval (CBIR) is a process in which for a given query image, similar images will be retrieved from a large image database based on their content similarity. The content of image refer to its features or attributes or parameters which are mathematically determined from a digital image. In this approach the images retrieved may not exactly match with the visually similar or semantically similar images. Semantic similarity refers how far the user expectation meets the retrieval. Content Based Image Retrieval gained its importance from early 1980's and still got lot of scope for the research community to find more sophisticated methods to improve the retrieval.[6] Content Based Image Retrieval got its significance in domain specific applications such as biomedical and satellite imaging etc.[7] In this paper we presented an exhaustive literature review of CBIR from its inception to till date, with all the new approaches that has included in this process. We present our review on benchmark image databases, color spaces which are used for implementation of CBIR process, image content as color, texture and shape attributes, and feature extraction techniques, similarity measures, feature set formation and reduction techniques, image indexing applied in the process of retrieval along with various classifiers with their effect in retrieval process, effect of relevance feedback and its importance in retrieval.},
        keywords = {Image Retrieval, Clustering, Classification, Feature Extraction, Optimization.},
        month = {May},
        }