Comprehensive Analysis on Image Search Engines

This study analyses image search engines., focussinHg on image quality issues and image retrieval system improvements. The quality of processed images now affects search engine performance due to the increased need for efficient image identification and retrieval systems. Content-based image retrieval (CBIR)., CNNs., and image enhancement approaches fix blurriness., low resolution., and noise in images. After a thorough literature analysis., the paper evaluates color histograms., wavelet transforms., and neural network architectures on INRIA Holidays and Corel datasets. Grey wolf optimization for feature extraction and sophisticated deblurring for image improvement are also featured in the survey. Response times., memory consumption., and the semantic gap in user intent are also addressed. Future image retrieval directions include merging deep learning methods with CBIR systems and expanding their applications in healthcare., e-commerce., and security.