A New Approach to Automated Text-Visual Analysis and Recognition using Human-Annotated Videos


A New Approach to Automated Text-Visual Analysis and Recognition using Human-Annotated Videos – Visual search in video images is becoming an important task in modern digital science. We demonstrate that an individual can often perform well on an image, due to low computational cost and high quality. We show that this state is beneficial in a general classification and recognition setting, where more accurate images have been identified from the search space, as well as that such high quality images can be used as a suitable training set for large learning datasets.

Deep convolutional network (DCNN) provides a powerful tool for video classification tasks, but it is expensive for standard datasets because of the high computation overhead. This paper proposes an efficient learning method based on deep learning for video classification tasks, which is built upon deep learning which is based on RGB-D data. To the best of our knowledge, this is the first work that utilizes deep learning for video classification tasks using RGB-D data. We compare the proposed method to the state-of-the-art methods, and demonstrate how to learn the features of RGB-D videos by using an efficient CNN. Our experiments show the benefit of learning RGB-D features for video classification tasks, especially for video sequences with challenging lighting and scene characteristics. We show that learning the features of RGB-D videos with RGB features leads to the best results, as compared to the current state-of-the-art methods. Moreover, we demonstrate the effectiveness of the proposed method on both RGB-D datasets with varying lighting conditions.

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A New Approach to Automated Text-Visual Analysis and Recognition using Human-Annotated Videos

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  • Learning to Rank Among Controlled Attributes

    Fusing Depth Colorization and Texture Coding to Decolorize ScenesDeep convolutional network (DCNN) provides a powerful tool for video classification tasks, but it is expensive for standard datasets because of the high computation overhead. This paper proposes an efficient learning method based on deep learning for video classification tasks, which is built upon deep learning which is based on RGB-D data. To the best of our knowledge, this is the first work that utilizes deep learning for video classification tasks using RGB-D data. We compare the proposed method to the state-of-the-art methods, and demonstrate how to learn the features of RGB-D videos by using an efficient CNN. Our experiments show the benefit of learning RGB-D features for video classification tasks, especially for video sequences with challenging lighting and scene characteristics. We show that learning the features of RGB-D videos with RGB features leads to the best results, as compared to the current state-of-the-art methods. Moreover, we demonstrate the effectiveness of the proposed method on both RGB-D datasets with varying lighting conditions.


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