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Kotenseki is a collection of classical and ancient Japanese literature. It is comprised of image books that express Japanese stories by using comic drawings of different characters, such as humans, nature, and animals. To effectively store them for posterity, a search system is important. We propose an efficient CBIR system to assist the users in easily accessing the information and have an enjoyable...
In this paper, a system to aid the visually impaired by providing contextual information of the surroundings using 360° view camera combined with deep learning is proposed. The system uses a 360° view camera with a mobile device to capture surrounding scene information and provide contextual information to the user in the form of audio. The scene information from the spherical camera feed is classified...
Deep learning has brought a series of breakthroughs in image processing. Specifically, there are significant improvements in the application of food image classification using deep learning techniques. However, very little work has been studied for the classification of food ingredients. Therefore, this paper proposes a new framework, called DeepFood which not only extracts rich and effective features...
In order to realize autonomous landing of the unmanned aerial vehicle (UAV) in power patrolling, a visual method vision based on Faster Regions with Convolutional Neural Network (Faster R-CNN) for UAVs is studied. In this paper, we design the landing sign of the combination of concentric circles and pentagon, and propose the Faster R-CNN recognition algorithm which can be used to identify the target...
As a typical deep learning method, Deep Belief Network (DBN) and Dropout method are usually used together for pattern recognition in case of lacking training data. Dropout training can avoid the overfitting phenomenon in deep neural network. During the testing stage, the outputs of all neurons in hidden layers are multiplied by a same factor as their actual outputs in the original Dropout method....
Visual tracking is a fundamental problem in computer vision. Recently, some deep-learning-based tracking algorithms have been achieving record-breaking performances. However, due to the high complexity of deep learning, most deep trackers suffer from low tracking speed, and thus are impractical in many real-world applications. Some new deep trackers with smaller network structure achieve high efficiency...
When beginners practice Chinese calligraphy, they often copy from ancient calligraphic works and try to imitate the style as closely as possible. However there are inevitably some characters whose styles are not correctly followed. Thus we are motivated to detect the style consistency of all written characters in one practice. With the styles extracted by using stacked autoencoders of deep neural...
Human action recognition is an imperative research area in the field of computer vision due to its numerous applications. Recently, with the emergence and successful deployment of deep learning techniques for image classification, object recognition, and speech recognition, more research is directed from traditional handcrafted to deep learning techniques. This paper presents a novel method for human...
In this paper we use a Deep Neural Network (DNN) trained on data collected from the visual media-sharing social platform Instagram account of a popular Indian lifestyle magazine to predict the popularity of future posts. This predicted popularity of the post can be used to decide advertising rates and measure performance metrics important for publishing strategy decisions. The DNN primarily uses growth...
Motivated by great performance gained by Recurrent neural network applied on machine translation, people began to pay attention to image describing with related deep learning methods. Recurrent neural network can not remember long term information but Long-Short Term Memory(LSTM) can handle this well. However, the LSTM applied on image describing to predict sentences in previous literature [1] can...
Automatic transcriptions of consumer generated multi-media content such as “Youtube” videos still exhibit high word error rates. Such data typically occupies a very broad domain, has been recorded in challenging conditions, with cheap hardware and a focus on the visual modality, and may have been post-processed or edited.
Inferring the aesthetic quality of images is a challenging computer vision task due to its subjective and conceptual nature. Most image aesthetics evaluation approaches focused on designing handcrafted features, and only a few adopted learning of relevant and imperative characteristics in a data-driven manner. In this paper, we propose to attune Convolutional Neural Networks (CNNs) for image aesthetics...
Recognition of traffic signs is vary important in many applications such as in self-driving car/driverless car, traffic mapping and traffic surveillance. Recently, deep learning models demonstrated prominent representation capacity, and achieved outstanding performance in traffic sign recognition. In this paper, we propose a traffic sign recognition system by applying convolutional neural network...
Scene classification of high resolution remote sensing images plays an important role for a wide range of applications. While significant efforts have been made in developing various methods for scene classification, most of them are based on handcrafted or shallow learning-based features. In this paper, we investigate the use of deep convolutional neural network (CNN) for scene classification. To...
The problem of extracting high level information from digital images and videos is frequently faced in the area of computer vision and machine learning. For the recognition of traffic signs, a lot of outstanding methods have been proposed, and deep models demonstrates that their powerful representation capacity, can archieve dominant performances. In this paper a method for recognizing traffic signs...
The deep learning of neural network works on vision recognition and classification tasks briskly, and it can extract great features of an image for classification. Recently, many approaches have studied the visual tracking in two-ways with these characteristics. First, they can regard tracking problem as classifying each video and frame by learning all dataset. Second, use the deep neural network...
For feature representation of pedestrian recognition, a hybrid hierarchical feature representation method which combines representation ability of bag of words model and depth layered with learning adaptability is presented. This method first uses HOG local descriptor for local features extraction, and then encoding the feature by a depth of layered coding method, the layered coding method by spatial...
In recent years different methods for detecting objectionable images have proposed. All of the previous systems are based on extracting pre-defined and certain features from the images. In this paper a method is proposed in order to detect objectionable images using convolutional neural networks. In this method first features are learned through a sparse auto-encoder and then training is done by a...
Due to the large amounts of Multimedia data on the Internet, Multimedia mining has become a very active area of research. Multimedia mining is a form of data mining. Data mining uses algorithms to segment data to identify useful patterns and to make predictions. Despite the successes in many areas, data mining remains a challenging task. In the past, multimedia mining was one of the fields where the...
Deep learning has shown great successes in solving various problems of computer vision. To the best of our knowledge, however, little existing work applies deep learning to saliency modeling. In this paper, a new saliency model based on convolutional neural network is proposed. The proposed model is able to produce a saliency map directly from an image's pixels. In the model, multi-level output values...
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