We propose a new method of retrieving multi-part, articulate objects from images and video. The scheme is particularly well suited for analyzing images and video for objects that can pose differently with possible shape deformation and articulated motion. The scheme involves computing an invariant signature for each segmented region in the image, in a manner that is insensitive to translation, rotation, scale, and shear. Using circular cross-correlation, these signatures can then be efficiently compared with that of user-defined regions of interest. Ambiguities between individual region matches are then resolved through relaxation labeling techniques. A final match is established when a collection of segmented regions conform to the query object, both in terms of local shape description and global structural relation. The scheme thus allows for articulated movement of object parts within the scene. The procedure is easy to implement, yet shows promising results in its ability to isolate interesting regions in images and video, to account for structural and relational constraints among regions, and to integrate both local shape and global structural information for a detailed examination of the scene in a way that is invariant to many visual variations.

We propose invariant formulations that can potentially be combined into a single system. In particular, we describe a framework for computing invariant features which are insensitive to rigid motion, affine transform, changes of parameterization and scene illumination, perspective transform, and view point change. This is unlike most current research on image invariants which concentrates on either geometric or illumination invariants exclusively. The formulations are widely applicable to many popular basis representations, such as wavelets, short-time Fourier analysis, and splines. Exploiting formulations that examine information about shape and color at different resolution levels, the new approach is neither strictly global nor local. It enables a quasi-localized, hierarchical shape analysis which is rarely found in other known invariant techniques, such as global invariants. Furthermore, it does not require estimating high-order derivatives in computing invariants (unlike local invariants), whence is more robust. We provide results of numerous experiments on both synthetic and real data to demonstrate the validity and flexibility of the proposed framework.