Bilinear Invariant Representation for Multimedia Information Classification and Retrieval
نویسندگان
چکیده
In this paper, we present a novel bilinear invariant representation for multimedia information classification and retrieval. We introduce the concept of kernel space from functional analysis to show that null space invariants are only the special case when the transformation is linear. Subsequently, we derive the exact invariant basis for one of the important applications for kernel space: bilinear invariants. We demonstrate that the proposed bilinear invariant basis provides a more powerful tool than the previous null space invariants in multimedia information retrieval and classification. The simulation results demonstrate that the proposed bilinear invariant basis provides the superior performance than traditional approaches for invariant multimedia information classification and retrieval, typically when the raw data is subject to linear transformations from different dimensions or with different dimensions in a multi-camera system.
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