With extensive usage of multimedia databases in real time applications, there arises a great need for developing efficient techniques to find the images from huge digital libraries. To find an image from a database, every image is represented with certain features. Texture and color are two important visual features of an image. In this paper we compare and analyze performance of image retrieval using texture and color features. Further we propose and implement an efficient image retrieval technique using both texture and color features of an image. Experimental evaluation is carried out on Wang image database having 1000 unique images consisting of 10 classes of images.
Messengers and social media dominate today’s internet usage across the globe. For the large population, a typical day starts with messages flooding on mobiles, from simple good morning wishes, business meeting invites, reminders, and schedules for the day and the list is endless. A striking feature of today’s digital communication is the variety of emojis used, without which text communication almost look incomplete. Emojis are graphic symbols/logograms used with text communication to enhance the effectiveness of emotions and set an undertone that makes texting a more fun experience for the users. Emojis are the visual language of the new generation. They give consumers a means to communicate their feelings while reducing the quantity of text that needs to be typed by the sender. Every social media and messenger platform like Facebook, Instagram, Twitter, WhatsApp, and many more have its own emoji set. To lure more and more users, many new emojis are added day by day. Predicting and suggesting emojis based on the text, emotion and user patterns to the user is an important feature of today’s messengers and social media applications. If you start typing a message, relevant emojis will be displayed from which users can choose an emoji, further enhancing the user texting experience. This process is done using natural language processing and machine learning techniques. In this paper, we study emoji prediction techniques and propose an emoji prediction model using bi-directional LSTMs. We compare emoji prediction NLP techniques, including RNN, LSTM, LSTM networks, and Bi-LSTM. Based on our implementation, we suggest that the bi-directional LSTM model is the most effective technique. Our model outperforms many baseline approaches with an accuracy of 94% when tested on a CodaLab Twitter data set with 60000 rows and two columns. Our study shows the effectiveness and efficiency of bi-directional LSTMs for text-based systems for communication.
Abstract: Real and Fake face recognition using CNN and deep learning is presented in the paper. Searching for the authenticity of an image with the naked eye becomes a complicated task in detecting image forgeries. The goal of this study is to evaluate how well different deep learning approaches perform. The initial stage of the proposed strategy is to train several pre-trained deep learning models on the image dataset for recognizing real and fake images to identify fake faces. In order to assess the effectiveness of these models, we consider how well they separate two classes - false and true. Regarding the models tested so far, the VGG models have the best training accuracy (86%) on VGG-16, while VGG-16 shows an excellent test set. accuracy with 10 epochs or less, which is competitively better than all other methods. The outputs of these models were examined to find out exactly
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