The demand for adaptive image retrieval is still an active research area, particularly in a dynamic environment. Erstwhile retrieval schemes ensure adaptivity by tuning the same image basis with wavelet transforms, in accordance with user’s significance. To enhance adaptivity and improve the retrieval performance, an Extremely Adaptive Image Retrieval (EAIR) scheme is presented that associates each image with different wavelet basis. This objective is achieved by building Characterization maps from wavelet coefficients of images (query and target) using higher order standardized moments of the Gamma function. The resulting maps are approximated by Volterra Series and later, mathematically programmed by Integral Global Optimization (IGO) algorithm for wavelet adaptation. Finally, the best wavelet filter for each query image is fitted using the Multivariate Adaptive Regression Splines (MARS). Characterization Maps rendered by EAIR achieves 60% reduction in Relative Approximation Error (RAE) with 11% decrease in query time observed under diverse dataset. Also, relative Precision-Recall (P-R), Precision at 5 (P5) analyses reveals a significant retrieval improvement of 9.52%, 1.35%, 1.12%, 8.07% by EAIR on Caltech, Messidor, AT&T, Vistex respectively. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.