Bilinear invariant representation for video classification and retrieval
Xu Chen, Dan Schonfeld, Ashfaq Khokhar · 2010
In this paper, we present a novel bilinear invariant representation for video classification and retrieval. We rely on the kernel space in functional analysis to formulate a general invariants theory. We show that null-space invariants is a special case of the general theory when the transformation is linear. Subsequently, we derive an invariant basis representation for bilinear transformations. We also extend the basis representation to tensor bilinear invariants. We demonstrate that the proposed bilinear invariant basis provides a much more powerful tool than null-space invariants for video classification and retrieval when the different data elements undergo distinct transformations. Simulation results illustrate the superior performance of the proposed bilinear invariant basis representation compared to traditional approaches to invariant video classification and retrieval.