Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks (1502.07209v2)

Abstract: In this paper, we study the challenging problem of categorizing videos according to high-level semantics such as the existence of a particular human action or a complex event. Although extensive efforts have been devoted in recent years, most existing works combined multiple video features using simple fusion strategies and neglected the utilization of inter-class semantic relationships. This paper proposes a novel unified framework that jointly exploits the feature relationships and the class relationships for improved categorization performance. Specifically, these two types of relationships are estimated and utilized by rigorously imposing regularizations in the learning process of a deep neural network (DNN). Such a regularized DNN (rDNN) can be efficiently realized using a GPU-based implementation with an affordable training cost. Through arming the DNN with better capability of harnessing both the feature and the class relationships, the proposed rDNN is more suitable for modeling video semantics. With extensive experimental evaluations, we show that rDNN produces superior performance over several state-of-the-art approaches. On the well-known Hollywood2 and Columbia Consumer Video benchmarks, we obtain very competitive results: 66.9\% and 73.5\% respectively in terms of mean average precision. In addition, to substantially evaluate our rDNN and stimulate future research on large scale video categorization, we collect and release a new benchmark dataset, called FCVID, which contains 91,223 Internet videos and 239 manually annotated categories.

• The paper introduces a novel methodology that leverages feature and class relationships to improve video categorization.
• It applies regularized deep neural networks to integrate multiple data aspects for robust video classification.
• The approach delivers promising improvements in accuracy, paving the way for further advancements in computer vision research.

Analysis of Document Content: FCVID Paper Review

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If this paper, presumably related to a topic at the intersection of video classification or computer vision (suggested by the acronym FCVID for Fudan-Columbia Video Dataset), were to be made fully available, an essay addressing its contributions, methodologies, and implications could be structured. Insights into dataset characteristics, algorithmic methodologies, evaluation metrics, and empirical outcomes would be integral to such a critique.

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