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Start date: 2010End date: 2019Type: search.filters.itemtype.Proceedings PaperSubject: search.filters.subject.TelecommunicationsSubject: search.filters.subject.deep learning

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Now showing 1 - 6 of 6
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    Automatically Inserting Ads into Images
    (IEEE, 2018) Citak, Erol; Eseoglu, Mustafa Furkan; Celik, Ibrahim Omer; Disanli, Onur; Kutluk, Sezer; Arica, Nafiz; Huawei Technologies; Bahcesehir University
    In this study a new method is proposed for inserting advertisement visuals into images automatically and without disturbing the image content. In this method important areas are determined using deep learning based object, face and text detection, edge and saliency maps are obtained, and these information are used for the identification of the best location for inserting the advertisement visual. In order to select the best available advertisement visual from an advertisement pool shape and color features are utilized.
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    An Ontology based Semantic Representation for Turkish Cuisine
    (IEEE, 2018) Ergun, Ovgu Ozturk; Ozturk, Bengu; Bahcesehir University; Yeditepe University
    Following recent advances in digital technologies, many data in various domains have been transformed into digital world and shared with millions of users via social media and web technologies. As a result, big amount of data has presented many challenging problems in different fields, e.g internet of things, artificial intelligence. One of application areas is in food domain. Recognition of food category from images, automatic recipe retrieval from internet and analysis and matching of food images with recipes, ingredients, nutrition values bring cooperation of multi disciplines and technologies. In this work, for the first time, semantical analysis of Turkish Cuisine is held and various information related to food in Turkish Cuisine is structured in a hierarchical ontology model. A new database containing 50 different food categories and related images is constructed and linked with data such as food properties, recipes, etc.. As a result, multimodal information retrieval can be achieved faster in a more semantic way. At the same time, food image classification with deep learning methods is performed and faster connection of recognized food category to related semantic data is provided.
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    Facial Emotion Classification Using Deep Embedding with Triplet Loss Function
    (IEEE, 2017) Bircanoglu, Cenk; Arica, Nafiz; Bahcesehir University
    In this paper, a deep embedding method using triplet loss function is proposed for classification of the emotions in face images. The originality of proposed method lies in the loss function, different from the other deep learning-based facial emotion classification approaches. The input face images are embedded into a lower dimensional feature space using a multi layer convolutional neural network. In this embedding process the loss function is calculated by taking triple samples in the training dataset. For each sample in batches, two samples are selected in such a way that one of them is from the same class and the other one from the different class. The loss function aims to close the samples belonging to the same class to each other in the Euclidean space and to move away from the samples in the different class. In the performance analysis of proposed method, two popular deep architectures, namely AlexNet and VGG are used on two datasets called CIFE and GaMo. The experiments show that the proposed method outperforms the other popular loss functions and the available studies on this particular datasets.
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    Auto-encoder based Cognitive Analysis of Questionnary
    (IEEE, 2018) Akay, Simge; Korkmaz, Hande; Arica, Nafiz; Bahcesehir University; Bahcesehir University
    In this study we propose a new method for Likert scale questionnary data analysis using auto-encoders. The proposed method extracts the patterns, which maximally activate the neurons of the hidden layer in the auto-encoder, trained by the questionnary data. These patterns are thought to be cognitive patterns that are influenced by participants filling the questionnary. In the experiments, we employ a questionnary designed to measure the confidence level of a blog author on the web. The cognitive patterns obtained in the auto-encoder are considered as the components that form the general approaches of the participants. In two cognitive patterns drawn from the questionnary, it is observed that the blogger's expertise, integrity, benevolance are evaluated in decreasing or increasing order by the participants. It has also been observed that the proposed method can be used to correct the unintentional mistakes in questionnary answers.
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    Face Frontalization Enhanced by Deep Learning
    (IEEE, 2017) Celik, Anil; Arica, Nafiz; Bahcesehir University
    In this study, a new approach based on 3-D models and deep learning to frontalize face images is proposed. Specifically designed for facial expression analysis, the proposed approach aims to reduce possible negative effects that a posed face image can generate, by normalizing the face region. In the first phase, the face image is semi-frontalized, with a pre-established 3-D reference model based approach. Then, missing regions on semi-frontalized images due to geometric transformation are reconstructed with the help of a denoising stacked autoencoder network. In this phase, missing regions created by line of sight are learned, with a deep architecture, using numerous images. When examined, it can be said that, faces acquired with the proposed approach, are objectively better than the faces acquired with a deep learning or 3-D based method alone. Therefore, it is assumed that the proposed approach can be used in face based computer vision methods as a beneficial pre-processing step.
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    A Comparison of Activation Functions in Artificial Neural Networks
    (IEEE, 2018) Bircanoglu, Cenk; Arica, Nafiz; Bahcesehir University
    In this study, the effects of Activation Functions (AF) in Artificial Neural Network (ANN) on regression and classification performance are compared. In comparisons, success rates in test data and duration of training are evaluated for both problems. A total of 11 AF functions, 10 AF commonly used in the literature and Square function proposed in this study, are compared using 7 different datasets, 2 for regression and 5 for classification. 3 different ANN architectures, which are considered to be the most appropriate for each dataset are employed in the experiments. As a result of totally 231 different training procedures, the effects of Afs are examined for different datasets and architectures. Similarly, the effects of AF on training time are shown for different datasets. In the experiments it is shown that ReLU is the most succesfull AF in general purposes. In addition to ReLU, Square function gives the better results in image datasets.