X-VS: Crossbar-Based Processing-in-Memory Architecture for Video Summarization

Challapalle, Nagadastagiri; Chandran, Makesh; Rampalli, Sahithi; Narayanan, Vijaykrishnan

doi:10.1109/isvlsi49217.2020.00091

Cited by 1 publication

(1 citation statement)

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“…The model uses information like click rates, semantic relevance, textual-space to visual-space mapping and feature classification using CNNs in order to achieve classification accuracy of 81%, which makes the system deployable for real-time use cases. Another novel model that uses user-ranking for video segments in order to summarize the video is proposed in the studies [19][20][21]. This model uses a combination of 2D CNNs, 1D CNNs and LSTMs in order to generate a refined importance score for each video segment.…”

Section: Literature Review Deep Learning-based Video Summarizationmentioning

confidence: 99%

Gated Recurrent Units and Recurrent Neural Network Based Multimodal Approach for Automatic Video Summarization

Kaur¹,

Aljrees²,

Kumar³

et al. 2023

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A typical video record aggregation system requires the concurrent performance of a large number of image processing tasks, including but not limited to image acquisition, pre-processing, segmentation, feature extraction, verification, and description. These tasks must be executed with utmost precision to ensure smooth system performance. Among these tasks, feature extraction and selection are the most critical. Feature extraction involves converting the large-scale image data into smaller mathematical vectors, and this process requires great skill. Various feature extraction models are available, including wavelet, cosine, Fourier, histogram-based, and edge-based models. The key objective of any feature extraction model is to represent the image data with minimal attributes and no loss of information. In this study, we propose a novel feature-variance model that detects differences in video features and generates feature-reduced video frames. These frames are then fed into a GRU-based RNN model, which classifies them as either keyframes or non-keyframes. Keyframes are then extracted to create a summarized video, while non-keyframes are reduced. Various key-frame extraction models are also discussed in this section, followed by a detailed analysis of the proposed summarization model and its results. Finally, we present some interesting observations about the proposed model and suggest ways to improve it.

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Section: Literature Review Deep Learning-based Video Summarizationmentioning

confidence: 99%