Fast Best-Match Shape Searching in Rotation-Invariant Metric Spaces

Object recognition and content-based image retrieval systems rely heavily on the accurate and efficient identification of 2-D shapes. Features such as color, texture, positioning etc., are insufficient to convey the information that could be obtained through shape analysis. A fundamental requirement in this analysis is that shape similarities are computed invariantly to basic geometric transformations, e.g., scaling, shifting, and most importantly, rotations. And while scale and shift invariance are easily achievable through a suitable shape representation, rotation invariance is much harder to deal with. In this work, we explore the metric properties of the rotation-invariant distance measures and propose an algorithm for fast similarity search in the shape space. The algorithm can be utilized in a number of important data mining tasks such as shape clustering and classification, or for discovering of motifs and discords in large image collections. The technique is demonstrated to introduce a dramatic speed-up over the current approaches, and is guaranteed to introduce no false dismissals.