We propose a novel unsupervised multiresolution adaptive and progressive gradient-based color-image segmentation algorithm (MAPGSEG) that takes advantage of gradient information in an adaptive and progressive framework. The proposed methodology is initiated with a dyadic wavelet decomposition scheme of an arbitrary input image accompanied by a vector gradient calculation of its color-converted counterpart in the 1976 Commission Internationale de l’Eclairage (CIE) color space. The resultant gradient map is used to automatically and adaptively generate thresholds to segregate regions of varying gradient densities at different resolution levels of the input image pyramid. At each level, the classification obtained by a progressively thresholded growth procedure is integrated with an entropy-based texture model by using a unique region-merging procedure to obtain an interim segmentation. A confidence map and nonlinear spatial filtering techniques are combined, and regions of high confidence are passed from one resolution level to another until the final segmentation at the highest (original) resolution is achieved. A performance evaluation of our results on several hundred images with a recently proposed metric called the normalized probabilistic Rand index demonstrates that the proposed work computationally outperforms published segmentation techniques with superior quality.