An Efficient Approach to Image Indexing and Retrieval Using Haar Cascade and Perceptual Similarity Index

Abstract

Image retrieval is a critical task in many fields such as indexing and Content-Based Image Retrieval is a tried and tested method for image retrieval. With advances in graphics and visualization techniques, image retrieval should be easy, quick and flexible. The aim of this work is to develop an efficient image indexing and retrieval system to collate similar images or images based on conditioned queries in the form of an image itself. The indexing method has been performed using wavelet transform and related mechanisms. Its detailing can easily entail description from a broad range to a narrow range. The indexing algorithm applies a Haar Wavelet-Based Perceptual Similarity Index (HaarPSI) for Image Quality Assessment. The HaarPSI utilizes the coefficients obtained from a Haar wavelet decomposition to assess local similarities between two images, as well as the relative importance of image areas. Results show that the algorithm used is able to retrieve and display images with high retrieval accuracy and medium relevance.The proposed methodology has been tested against known image retrieval techniques and was found to be comparable and in some cases better than most prior known techniques. The testing was performed on images of varied types, from multiple datasets and sizes. The accuracy was found to be 99% in most cases. All in all, this study introduces a simple and convenient way of offline image searches on desktop computers and provides a stepping stone to future content-based image retrieval systems built for similar purposes.