A Novel Key-Frame Extraction Approach for Both Video Summary and Video Index

Lei, Shaoshuai; Xie, Gang; Yan, Gaowei

doi:10.1155/2014/695168

Cited by 18 publications

(8 citation statements)

References 17 publications

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“…Although image caption can be applied to image retrieval [92], video caption [93,94], and video movement [95] and the variety of image caption systems are available today, experimental results show that this task still has better performance systems and improvement. It mainly faces the following three challenges: first, how to generate complete natural language sentences like a human being; second, how to make the generated sentence grammatically correct; and third, how to make the caption semantics as clear as possible and consistent with the given image content.…”

Section: Resultsmentioning

confidence: 99%

An Overview of Image Caption Generation Methods

Wang

Zhang

2020

Computational Intelligence and Neuroscience

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In recent years, with the rapid development of artificial intelligence, image caption has gradually attracted the attention of many researchers in the field of artificial intelligence and has become an interesting and arduous task. Image caption, automatically generating natural language descriptions according to the content observed in an image, is an important part of scene understanding, which combines the knowledge of computer vision and natural language processing. The application of image caption is extensive and significant, for example, the realization of human-computer interaction. This paper summarizes the related methods and focuses on the attention mechanism, which plays an important role in computer vision and is recently widely used in image caption generation tasks. Furthermore, the advantages and the shortcomings of these methods are discussed, providing the commonly used datasets and evaluation criteria in this field. Finally, this paper highlights some open challenges in the image caption task.

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Section: Resultsmentioning

confidence: 99%

An Overview of Image Caption Generation Methods

Wang

Zhang

2020

Computational Intelligence and Neuroscience

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“…The features extracted from the deep CNN based work 12,24,30,34,36 represent the higher‐level abstraction of visual concepts on high dimensional input and ignores to represent the lower‐level visual concepts. To address this issue, spatial feature fusion network (SFFN) is proposed which is composed of Core Feature extraction and SFFN to extracts the per‐frame feature vector by exploiting both the low‐level and high‐level visual concepts from low dimensional input for each keyframe extracted by using 37 …”

Section: Proposed Methodsmentioning

confidence: 99%

Spatio‐temporal feature fusion based correlative binary relevance for visual object detection

Amaresh

Chitrakala

2021

Concurrency and Computation

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Object detection in video is an emerging and challenging research topic in Computer Vision. Recently, deep learning based approaches achieves greater results in object detection. The major issues in detecting multiple objects from the videos are exploiting low‐level visual concepts and temporal information and also exploring the correlation between the objects in the videos. Therefore, to address the above mentioned issues, the spatio‐temporal feature fusion based correlative binary relevance (STFF‐CBR) classifier is proposed to generate a rich vector representation and exploit the label correlation for the object detection in videos. In this article, first, the spatio‐temporal feature fusion (STFF) is proposed to exploit the low‐level visual concepts and temporal information in the videos which significantly improves the object detection performance. Second, correlative binary relevance (CBR) classification approach is proposed to exploit the dependencies between the labels in the video using the nearest neighbor based label dependency graph (LDG). Additionally, feed forward neural network (FFNN) classifier is utilized to increase the classification accuracy of the CBR method. Experimental evaluation shows that the STFF‐CBR classifier model achieves better performance for object detection problem in video against the state‐of‐the‐art methods.

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“…A step in this approach is automating information extraction from egocentric video in the form of graphical collections or key-frames. In terms of content, key-frame (representative frame) extraction entails extracting the most informative frames that encapsulate the essential events in a video [3]. For storage, indexing, and retrieval, it is necessary to remove duplicate information from long video sequences [4].…”

Section: Introductionmentioning

confidence: 99%

Egocentric Hand-object Interaction Detection

Lu¹,

Liu²

2022

Preprint

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In this paper, we propose a method to jointly determine the status of hand-object interaction. This is crucial for egocentric human activity understanding and interaction. From a computer vision perspective, we believe that determining whether a hand is interacting with an object depends on whether there is an interactive hand pose and whether the hand is touching the object. Thus, we extract the hand pose, handobject masks to jointly determine the interaction status. In order to solve the problem of hand pose estimation due to inhand object occlusion, we use a multi-cam system to capture hand pose data from multiple perspectives. We evaluate and compare our method with the most recent work from Shan et al.[1] on selected images from EPIC-KITCHENS [2] dataset and achieve 89% accuracy on HOI (hand-object interaction) detection which is comparative to Shan's (92%). However, for real-time performance, our method can run over 30 FPS which is much more efficient than Shan's (1 ∼ 2 FPS). A demo can be found from https://www.youtube.com/watch?v=XVj3zBuynmQ.

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A Novel Key-Frame Extraction Approach for Both Video Summary and Video Index

Cited by 18 publications

References 17 publications

An Overview of Image Caption Generation Methods

An Overview of Image Caption Generation Methods

Spatio‐temporal feature fusion based correlative binary relevance for visual object detection

Egocentric Hand-object Interaction Detection

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