SIMPLICity is a content-based image retrieval system that integrates semantics-sensitive classification, wavelet-based feature extraction, and integrated region matching for image segmentation. The system classifies images into semantic categories such as textured-nontextured and graph-photograph, enabling semantically-adaptive searching. It uses an overall similarity measure that integrates properties of all regions in images, reducing the adverse effects of inaccurate segmentation and clarifying regional semantics. Compared to region-based retrieval, SIMPLICity provides a simple querying interface and improves retrieval performance and speed. The system is robust to image alterations and has been tested on large databases, demonstrating superior performance. The system uses a k-means algorithm for image segmentation, extracting features such as color, texture, and shape. It classifies images into semantic categories like textured versus nontextured and graph versus photograph. The Integrated Region Matching (IRM) similarity measure combines region properties to provide robustness against segmentation errors. The IRM measure is defined as a weighted sum of region pair similarities, with weights determined by the matching scheme. The system uses a significance matrix to assign importance to region matches, ensuring that the most similar regions are prioritized. The IRM distance is calculated as the sum of weighted edge lengths in a graph representing region matches. The system also defines distance between region pairs based on color, texture, and shape features, with shape distance adjusted by a converting function to ensure proper influence on total distance. The IRM measure is robust to segmentation errors and provides accurate image retrieval by integrating region properties.SIMPLICity is a content-based image retrieval system that integrates semantics-sensitive classification, wavelet-based feature extraction, and integrated region matching for image segmentation. The system classifies images into semantic categories such as textured-nontextured and graph-photograph, enabling semantically-adaptive searching. It uses an overall similarity measure that integrates properties of all regions in images, reducing the adverse effects of inaccurate segmentation and clarifying regional semantics. Compared to region-based retrieval, SIMPLICity provides a simple querying interface and improves retrieval performance and speed. The system is robust to image alterations and has been tested on large databases, demonstrating superior performance. The system uses a k-means algorithm for image segmentation, extracting features such as color, texture, and shape. It classifies images into semantic categories like textured versus nontextured and graph versus photograph. The Integrated Region Matching (IRM) similarity measure combines region properties to provide robustness against segmentation errors. The IRM measure is defined as a weighted sum of region pair similarities, with weights determined by the matching scheme. The system uses a significance matrix to assign importance to region matches, ensuring that the most similar regions are prioritized. The IRM distance is calculated as the sum of weighted edge lengths in a graph representing region matches. The system also defines distance between region pairs based on color, texture, and shape features, with shape distance adjusted by a converting function to ensure proper influence on total distance. The IRM measure is robust to segmentation errors and provides accurate image retrieval by integrating region properties.