Acute shape characterization in image retrieval tasks remain a persisting issue in computer vision determining their retrieval performance. This chapter contributes and relatively compares three such descriptors that are further tested for shape classification by employing a supervised machine learning mechanism. The core objective of this chapter is the effective exploitation of simple computing concepts for realizing shape descriptors aiding retrieval. Accordingly, simple and novel shape descriptors with its performance analysis are presented in this chapter. The potency of these methods is investigated using the Bull’s Eye Retrieval (BER) rate on benchmarked datasets such as the Kimia, MPEG-7 CE Shape-1 part B and Tari-1000. Consistent BER greater than 90% attained across the diverse datasets affirms the descriptors efficacy, consequently signifying the robustness of these descriptors towards diverse affine transformations thereby, making it suitable for dynamic CBIR applications. © 2020, Springer Nature Switzerland AG.