Methods of multiresolutional image representation are well known and widely used in computer vision. They have many serious advantages because they provide compact encoding of images and quick search of the objects within the images. However, the traditional methods of multiresolutional image representation presume forming a set of copies of the image at different resolution levels with a constant resolution at each level and constant ratio between two consecutive levels which does not depend on the structure of the processed image. On the other hand, it has already been demonstrated that every image has a limited and innate spatial scale range as well as a limited resolution range, and the result of transformation depends on their initial selection. The main idea of the developed approach is not to form constant resolution levels but rather to grOw a resolution tree which depends on the structure of the image and the task at hand. The resolution tree grows sequentially during the topdown processing of the image. As a primary feature, we have selected an oriented edge segment (a stroke). The strokes are extracted with a particular resolution of the level at the points of local maxima of the brightness gradient. Strokes can have different thickness, length, and orientation. Each of them subsequently is decomposed into another set of strokes at a higher resolution, and so on. Thus, the whole gray-level image is being transformed into a hierarchical set of strokes characterizing forms of objects with different degree of generalization depending on the size of objects in the images. This method will transform the original image to a hierarchical graph which allows for efficient coding in order to store, retrieve, and recognize the image. The method which is proposed is based upon finding the resolution levels corresponding to each image, and each subset of the image individually which minimizes the computations required. This becomes possible because of the use of a special image representation technique called Multiresolutional Attentional Representation for Recognition. This feature turns out to be efficient in the process of finding the appropriate system of resolutions and construction of the relational graph. The process is performed by a three-layer neural network (NN) with intra-layer interactions between neurons, the receptive fields of which are selectively tuned to detect the orientations of local contrasts in parts of the image with appropriate degree of generalization. The NN forms a stroke-sketch of corresponding resolution for each partition of the image.The scale parameter characterizing the resolution of processing is assigned to NN from outside. In order to correct the stroke-sketch formed by the NN, the bottoin.up algorithm is used to complete the stroke-sketch with missing strokes. Three supportive algorithms make possible this top-down/bottom-up operation. The Resolution Level Estimation (RLE) Algorithm for apriori evaluation of the preferable scale parameter for the whole image and all of its partitions. The Hierarchical Image Decomposition (HID) Algorithm finds partitions. Finally, the Algorithm of Corrections by Gestalt Heuristics (CGH) provides the "gestalt" unity of the strokes that may have been "damaged" during processing.