Human visual recognition is based largely on shape, yet effectively using shapes in natural image retrieval is a challenging task. Most existing methods are based on the geometric equations of curves computed from processing an entire image. These processes are computationally intensive, lack flexibility and do not take advantage or with minimum use of the Gestalt rules of human vision. By applying certain mechanisms based on the human visual perception process, our methods extract generic shape features from real world images. We extract and group perceptually significant segments and use their properties to create a Euclidean distance matrix for image retrieval. As all the computing is based on simple calculation and one pixel width edges instead of the whole image, this method provides a novel and efficient image feature representation. Testing on standard benchmark datasets and comparison with other well-known methods show this shape analysis method using only compact feature vectors performs well and robustly for real world images.