Transform invariant video fingerprinting by NMF

Video fingerprinting is introduced as an effective tool for identification and recognition of video content even after putative modifications. In this paper, we present a video fingerprinting scheme based on non-negative matrix factorization (NMF). NMF is shown to be capable of generating discriminative, parts-based representations while reducing the dimensionality of the data. NMF's representation capacity can be fortified by incorporating geometric transformational duplicates of the base vectors into the factorization. Factorized base vectors are used as content based, representative features that uniquely describe the video content. Obtaining such base vectors by transformational NMF (T-NMF) is furthermore versatile in recognizing the attacked contents as copies of the original instead of considering them as a new content. Thus a novel approach for fingerprinting of video content based on T-NMF is introduced in this work and experimental results obtained on TRECVID data set are presented to demonstrate the robustness to geometric attacks and the improvement in the representation.