IEEE Trans Image Process - Common and Innovative Visuals: A sparsity modeling framework for video.

Tópicos

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Resumo

Efficient video representation models are critical for many video analysis and processing tasks. In this paper, we present a framework based on the concept of finding the sparsest solution to model video frames. To model the spatio-temporal information, frames from one scene are decomposed into two components: (i) a common frame, which describes the visual information common to all the frames in the scene/segment, and (ii) a set of innovative frames, which depicts the dynamic behaviour of the scene. The proposed approach exploits and builds on recent results in the field of compressed sensing to jointly estimate the common frame and the innovative frames for each video segment. We refer to the proposed modeling framework by CIV (Common and Innovative Visuals). We show how the proposed model can be utilized to find scene change boundaries and extend CIV to videos from multiple scenes. Furthermore, the proposed model is robust to noise and can be used for various video processing applications without relying on motion estimation and detection or image segmentation. Results for object tracking, video editing (object removal, inpainting) and scene change detection are presented to demonstrate the efficiency and the performance of the proposed model.

Resumo Limpo

effici video represent model critic mani video analysi process task paper present framework base concept find sparsest solut model video frame model spatiotempor inform frame one scene decompos two compon common frame describ visual inform common frame sceneseg ii set innov frame depict dynam behaviour scene propos approach exploit build recent result field compress sens joint estim common frame innov frame video segment refer propos model framework civ common innov visual show propos model can util find scene chang boundari extend civ video multipl scene furthermor propos model robust nois can use various video process applic without reli motion estim detect imag segment result object track video edit object remov inpaint scene chang detect present demonstr effici perform propos model

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