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Video Summarization using Deep Semantic Features

Mayu Otani, Yuta Nakashima, Esa Rahtu, Janne Heikkilä, Naokazu Yokoya

arXiv:1609.08758v1cs.CV

TL;DR

Internet videos are diverse, and identifying important segments requires understanding their content. The paper learns deep semantic features by jointly mapping videos and descriptions, then uses clustering-based summarization; its summaries score higher than VGG-based summaries against manually created summaries.

  • Problem

    Internet videos span diverse content, making it challenging to identify informative segments when understanding their content is required.

    Source: §1 · pp. 1–2

  • Method

    A neural network jointly maps videos and descriptions into a semantic space, whose segment features are used for clustering-based summary generation.

    Source: §1 · p. 3

  • Results

    58.8% of the average score of manually created video summaries was achieved by the proposed summaries, versus 40.8% for VGG-based summaries.

    Source: §4.2 · p. 12

  • Takeaways & Limitations

    The comparison with manually created summaries supports an advantage from incorporating the learned deep features into video summarization.

    Source: §5 · p. 13

    Uniform 5-second segments can leave short videos with few segments and lower f-measure scores.

    Source: §4.2 · p. 13

Abstract

from arXiv · show

This paper presents a video summarization technique for an Internet video to provide a quick way to overview its content. This is a challenging problem because finding important or informative parts of the original video requires to understand its content. Furthermore the content of Internet videos is very diverse, ranging from home videos to documentaries, which makes video summarization much more tough as prior knowledge is almost not available. To tackle this problem, we propose to use deep video features that can encode various levels of content semantics, including objects, actions, and scenes, improving the efficiency of standard video summarization techniques. For this, we design a deep neural network that maps videos as well as descriptions to a common semantic space and jointly trained it with associated pairs of videos and descriptions. To generate a video summary, we extract the deep features from each segment of the original video and apply a clustering-based summarization technique to them. We evaluate our video summaries using the SumMe dataset as well as baseline approaches. The results demonstrated the advantages of incorporating our deep semantic features in a video summarization technique.

1 Introduction

Internet video metadata may not represent a video's full content, while its diverse concepts make important segments difficult to identify. The paper uses description-trained semantic features to select representative, nonredundant segments and reports an advantage over VGG-based features.

1 Introduction

  • Video metadata can be incomplete or irrelevant, so viewers may need to review retrieved videos to understand their content.Summaries can provide compact retrieval results that help users quickly find desired videos.Source: §1 · pp. 1–2
  • Internet videos span objects, actions, and scenes, which low-level features and predefined concept sets do not adequately capture.Enumerating all possible concepts and designing detectors for them is described as almost infeasible.Source: §1 · p. 2
  • The method extracts deep features for short video segments, then samples semantically representative, nonredundant segments and concatenates them in temporal order.An objective function evaluates representativeness and redundancy.Source: §1 · p. 3
  • A jointly trained network maps videos and descriptions into a semantic space, using associated pairs to capture concepts at multiple semantic levels.Descriptions can combine objects, actions, and scenes, as in “a man is playing the guitar on stage.”Source: §1 · p. 3
  • The paper presents jointly trained deep features for summarization and compares their benefit with features extracted using VGG.The introduction characterizes this as the first attempt to use jointly trained deep features for video summarization.Source: §1 · p. 3

2 Related Work

Prior summarization approaches use domain knowledge, heuristic criteria, or retrieval-based cues to select important segments. This paper instead emphasizes semantic feature design, pairing videos with descriptions so clustering-based extraction can handle higher-level concepts.

2 Related Work

  • Domain-specific methods use events, structure, or metadata, but their reliance on domain knowledge makes generalization to other genres difficult.Examples include sports events, movie characters, and predefined objects in egocentric video.Source: Video Summarization · p. 4
  • Unsupervised methods formulate summarization as optimization over criteria such as reconstruction error or segment diversity.The criteria are often determined heuristically, and manually creating good summaries limits dataset scalability.Source: Video Summarization · p. 4
  • Canonical-view methods use images or videos retrieved with a video's title or keywords, but retrieval can be expensive and difficult for rare concepts..Source: Video Summarization · p. 4
  • Rather than relying on sophisticated extraction over low-level features, this paper prioritizes richer semantic features to reduce redundancy across different appearances.Low-level features may fail to distinguish semantically identical content under different viewpoints or lighting.Source: Video Summarization · p. 5
  • Joint video-description training is intended to encode sentence-level semantics, enabling clustering-based extraction to handle higher-level concepts.The representation contrasts with features trained to predict labels from a specific domain.Source: Representation Learning · p. 5

3 Approach

The approach represents uniform video segments in a semantic space and selects cluster-center segments to form a summary. It learns video features jointly with associated descriptions, then uses k-medoids to choose representative segments.

3 Approach

  • Uniform video segments are mapped to semantic-space features, and a subset is selected by optimizing representativeness before being concatenated in temporal order.The pipeline uses a trained DNN for feature extraction and samples segments corresponding to cluster centers.Source: §3 · p. 5, Figure 2 · p. 6

3.1 Learning Deep Features

  • Video and description subnetworks are jointly trained with contrastive loss to place associated pairs closer than irrelevant pairs, aiming to encode objects, actions, and scenes.The video branch is a modified VGG with two fully connected layers followed by mean pooling; the description branch uses an RNN-based sentence representation.Source: §3 · p. 7, §3.1 · pp. 7–8
  • The learned embedding places semantically relevant videos closer together, with the plotted examples grouping cooking and sports clips.The visualization reduces feature dimensionality with t-SNE; it illustrates the learned embedding rather than a benchmark comparison.Source: Figure 5 · p. 8, §3.1 · p. 9

3.2 Generating Video Summary

  • K-medoids selects K segment features minimizing total distance to the nearest selected center, then the corresponding segments are concatenated in temporal order.K determines the summary length; cluster centers are intended to yield representative, diverse segments.Source: §3.2 · p. 9

3.3 Implementation Detail

4 Experiment

The approach was evaluated on SumMe against human summaries and computational baselines. Its summaries scored higher than VGG-based summaries and showed agreement with annotators, though several video types remained challenging.

4.1 Baselines

  • The evaluation used 25 SumMe videos and compared against human summaries, uniform sampling, VGG-based summaries, attention, and interestingness.The dataset provides manually created summaries used as references; the best-human summary is the one with the highest average F-measure against the others.Source: §4 · p. 10, §4.1 · pp. 10–11

4.2 Results

  • Segments selected by our approach correlated with annotators’ selections, suggesting agreement between the algorithm and human annotators.Figure 6 marks selected segments in green and annotator selection ratios with blue lines.Source: §4.2 · p. 11, Figure 6 · p. 11
  • 58.8% of the average manually created-summary score was achieved by our approach, versus 40.8% for VGG-based summaries.Table 1 reports F-measures, with higher scores better.Source: Table 1 · p. 12, §4.2 · p. 12
  • Interestingness scored highest overall; our approach beat attention-based summaries on mean F-measure and outperformed interestingness on some videos.The interestingness approach optimizes objective-function weights using the SumMe dataset.Source: §4.2 · pp. 12–13
  • Fixed 5-second segments leave short videos with few candidates, while long unimportant stretches and diverse content also challenge the approach.The paper notes low scores for short videos and difficulty excluding unimportant segments or choosing among many possible segments.Source: §4.2 · p. 13
  • In examples, our summary captures the performance scene in “Cooking,” but selects unimportant static scenes in “Car over Camera.”The latter video’s static ground and sky scenes form large semantic clusters, whose representatives are selected by the approach.Source: §4.2 · p. 13

5 Conclusion

The paper learns semantic video features from video-description pairs and uses them to select representative segments for summaries. On SumMe, its summaries outperformed the worst human-created summaries; the authors identify additional objective criteria as a possible extension.

5 Conclusion

  • The method learns semantic deep features and selects video segments that represent clusters in the semantic feature space.A two-subnetwork DNN for videos and descriptions is jointly trained with contrastive loss.Source: §5 · p. 13
  • The summaries outperformed the worst human-created summaries in the reported comparison.The authors state that incorporating their deep features advantages video summarization.Source: §5 · p. 13
  • The objective function could be extended to include criteria such as interestingness and temporal uniformity.Source: §5 · p. 14
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