Video retrieval system using parallel multi-class recurrent neural network based on video description
2018 14th international conference on emerging technologies (ICET), 2018•ieeexplore.ieee.org
In recent times, there has been continuous interest in the area of content based information
retrieval (CBIR) for images and video sequences. Exponential increase of multimedia data
has triggered a cause for managing, storing and retrieving multimedia contents in
convenient and efficient ways. Visual features from static images and dynamic videos are
extracted to perform retrieval task. Once visual features are extracted, there is a need to
search and retrieve relevant videos in efficient amount of time. This paper makes use of …
retrieval (CBIR) for images and video sequences. Exponential increase of multimedia data
has triggered a cause for managing, storing and retrieving multimedia contents in
convenient and efficient ways. Visual features from static images and dynamic videos are
extracted to perform retrieval task. Once visual features are extracted, there is a need to
search and retrieve relevant videos in efficient amount of time. This paper makes use of …
In recent times, there has been continuous interest in the area of content based information retrieval (CBIR) for images and video sequences. Exponential increase of multimedia data has triggered a cause for managing, storing and retrieving multimedia contents in convenient and efficient ways. Visual features from static images and dynamic videos are extracted to perform retrieval task. Once visual features are extracted, there is a need to search and retrieve relevant videos in efficient amount of time. This paper makes use of seven visual features; human detection, emotion, age, gender, activity, scene and object detection followed by sentence generation. Furthermore, generated sentence is used in multi-class recurrent neural network (RNN) to find genre of a video for retrieval task. Accuracy, precision and recall are used for evaluation of this framework on self generated dataset. Experiments show that our system is able to achieve high accuracy of 88.13%.
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