We present an optimal approach to unsupervised color image clustering, suited for high resolution images based on mode seeking by mediod shifts. It is shown that automatic detection of total number of clusters depends upon overall image statistics as well as the bandwidth of the underlying probability density function. An optimized adaptive mode seeking algorithm based on reverse parallel tree traversal is proposed. This work has contribution in three aspects. 1) Adaptive bandwidth for kernel function is proposed based on the overall image statistics; 2) A novel reverse parallel tree traversing approach for mode seeking is presented which drastically reduces number of computational steps as compared to traditional tree traversing. 3) For high resolution images block clustering based optimized Adaptive Mediod Shift (AMS) is proposed where mode seeking is done in blocks and then the local modes are merged globally. The proposed method has made it possible to perform clustering on variety of high resolution images. Experimental results have shown our algorithm time efficient and robust.