scholarly journals Employability of Neural Network Tools and Techniques for Enhancing Image Caption Generation

2020 ◽  
Vol 10 (4) ◽  
Author(s):  
Harshit Dua
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Language Processing ◽  
Surveillance System ◽  
Video Surveillance System ◽  
Neural Organization ◽  
Tools And Techniques ◽  
The Web ◽  
Image Caption Generation ◽  
Image Caption

Nowadays, there is massive research in generating automatic image caption; this technique is very challenging and uses Natural language processing. For instance, it could assist incapacitated people with improving the matter of images on the web. Likewise, it could give more precise and minimized images/recordings in situations, such as picture sharing in interpersonal organization or video surveillance system. The structure comprises a convolutional neural organization (CNN) traced by a repetitive neural organization (RNN). The strategy can produce picture sayings that are generally semantically unmistakable and linguistically right by taking in information from picture and subtitle matches. Individuals, for the most part, depict a scene utilizing characteristic languages which are concise and reduced. However, computer vision frameworks define the set by taking a picture which is a two-measurement presentation. The plan is to picture and engrave similar places and projects from the image to the sentences.

scholarly journals An Overview of Image Caption Generation Methods

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Haoran Wang ◽  
Yue Zhang ◽  
Xiaosheng Yu
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Rapid Development ◽  
Evaluation Criteria ◽  
Arduous Task ◽  
Image Caption Generation ◽  
Image Caption

In recent years, with the rapid development of artificial intelligence, image caption has gradually attracted the attention of many researchers in the field of artificial intelligence and has become an interesting and arduous task. Image caption, automatically generating natural language descriptions according to the content observed in an image, is an important part of scene understanding, which combines the knowledge of computer vision and natural language processing. The application of image caption is extensive and significant, for example, the realization of human-computer interaction. This paper summarizes the related methods and focuses on the attention mechanism, which plays an important role in computer vision and is recently widely used in image caption generation tasks. Furthermore, the advantages and the shortcomings of these methods are discussed, providing the commonly used datasets and evaluation criteria in this field. Finally, this paper highlights some open challenges in the image caption task.

scholarly journals Analysis of Different Neural Network Techniques Used for Image Caption Generation

2020 ◽  
Vol 9 (9) ◽  
pp. 299-304
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
State Of The Art ◽  
Human Life ◽  
Complex Problem ◽  
Problem Statement ◽  
Human Ability ◽  
Machine Interaction ◽  
Image Caption Generation ◽  
Image Caption

Artificial intelligence has open doors to new opportunities for research and development. And the recent development in machine learning and deep learning has paved a way to deal with complex problem easily. Now a day’s every aspect of human life can now be thought of as a problem statement that can be implemented and is useful in one way or the other. One such aspect is human ability to understand and describe the surrounding to which they interact and take decision accordingly. This ability can also be used in machines or bots to make human and machine interaction easier. Generating captions for images is same we need to describe images based on what you see. This task can be considered as a combination of computer vision and natural language processing. In this paper we performs a survey of various methods and techniques that can be useful in understanding how this task can be done. The survey mainly focuses on neural network techniques, because they give state of the art results.

scholarly journals Smart Video Surveillance System Based on Edge Computing

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2958
Author(s):  
Antonio Carlos Cob-Parro ◽  
Cristina Losada-Gutiérrez ◽  
Marta Marrón-Romera ◽  
Alfredo Gardel-Vicente ◽  
Ignacio Bravo-Muñoz
Keyword(s):  
Computer Vision ◽  
Video Surveillance ◽  
Surveillance System ◽  
Video Surveillance System ◽  
Smart Camera ◽  
People Detection ◽  
Camera System ◽  
Edge Node ◽  
New Processing ◽  
Processor Unit

New processing methods based on artificial intelligence (AI) and deep learning are replacing traditional computer vision algorithms. The more advanced systems can process huge amounts of data in large computing facilities. In contrast, this paper presents a smart video surveillance system executing AI algorithms in low power consumption embedded devices. The computer vision algorithm, typical for surveillance applications, aims to detect, count and track people’s movements in the area. This application requires a distributed smart camera system. The proposed AI application allows detecting people in the surveillance area using a MobileNet-SSD architecture. In addition, using a robust Kalman filter bank, the algorithm can keep track of people in the video also providing people counting information. The detection results are excellent considering the constraints imposed on the process. The selected architecture for the edge node is based on a UpSquared2 device that includes a vision processor unit (VPU) capable of accelerating the AI CNN inference. The results section provides information about the image processing time when multiple video cameras are connected to the same edge node, people detection precision and recall curves, and the energy consumption of the system. The discussion of results shows the usefulness of deploying this smart camera node throughout a distributed surveillance system.

An Efficient Optimal Neural Network-Based Moving Vehicle Detection in Traffic Video Surveillance System

2019 ◽  
Vol 39 (2) ◽  
pp. 734-756 ◽  
Author(s):  
Ahilan Appathurai ◽  
Revathi Sundarasekar ◽  
C. Raja ◽  
E. John Alex ◽  
C. Anna Palagan ◽  
...  
Keyword(s):  
Neural Network ◽  
Video Surveillance ◽  
Surveillance System ◽  
Vehicle Detection ◽  
Video Surveillance System ◽  
Moving Vehicle ◽  
Traffic Video ◽  
Moving Vehicle Detection

scholarly journals Middle-Level Attribute-Based Language Retouching for Image Caption Generation

2018 ◽  
Vol 8 (10) ◽  
pp. 1850 ◽  
Author(s):  
Zhibin Guan ◽  
Kang Liu ◽  
Yan Ma ◽  
Xu Qian ◽  
Tongkai Ji
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Middle Level ◽  
Generation Model ◽  
Image Description ◽  
Image Captioning ◽  
Benchmark Datasets ◽  
Intermediate Image ◽  
Image Caption Generation ◽  
Image Caption

Image caption generation is attractive research which focuses on generating natural language sentences to describe the visual content of a given image. It is an interdisciplinary subject combining computer vision (CV) and natural language processing (NLP). The existing image captioning methods are mainly focused on generating the final image caption directly, which may lose significant identification information of objects contained in the raw image. Therefore, we propose a new middle-level attribute-based language retouching (MLALR) method to solve this problem. Our proposed MLALR method uses the middle-level attributes predicted from the object regions to retouch the intermediate image description, which is generated by our language generation model. The advantage of our MLALR method is that it can correct descriptive errors in the intermediate image description and make the final image caption more accurate. Moreover, evaluation using benchmark datasets—MSCOCO, Flickr8K, and Flickr30K—validated the impressive performance of our MLALR method with evaluation metrics—BLEU, METEOR, ROUGE-L, CIDEr, and SPICE.

scholarly journals Image Caption Generator

2021 ◽  
Vol 10 (3) ◽  
pp. 87-92
Author(s):  
Megha J Panicker ◽  
Vikas Upadhayay ◽  
Gunjan Sethi ◽  
Vrinda Mathur
Keyword(s):  
Computer Vision ◽  
Language Processing ◽  
Network Models ◽  
Image Captioning ◽  
Neural Network Models ◽  
Visually Impaired People ◽  
Video Frames ◽  
Modern Era ◽  
Deep Learning Model ◽  
Image Caption

In the modern era, image captioning has become one of the most widely required tools. Moreover, there are inbuilt applications that generate and provide a caption for a certain image, all these things are done with the help of deep neural network models. The process of generating a description of an image is called image captioning. It requires recognizing the important objects, their attributes, and the relationships among the objects in an image. It generates syntactically and semantically correct sentences.In this paper, we present a deep learning model to describe images and generate captions using computer vision and machine translation. This paper aims to detect different objects found in an image, recognize the relationships between those objects and generate captions. The dataset used is Flickr8k and the programming language used was Python3, and an ML technique called Transfer Learning will be implemented with the help of the Xception model, to demonstrate the proposed experiment. This paper will also elaborate on the functions and structure of the various Neural networks involved. Generating image captions is an important aspect of Computer Vision and Natural language processing. Image caption generators can find applications in Image segmentation as used by Facebook and Google Photos, and even more so, its use can be extended to video frames. They will easily automate the job of a person who has to interpret images. Not to mention it has immense scope in helping visually impaired people.

scholarly journals Image Caption Generation and Comprehensive Comparison of Image Encoders

2021 ◽  
pp. 42-55
Author(s):  
Shitiz Gupta ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Language Processing ◽  
State Of The Art ◽  
Image Feature ◽  
Image Captioning ◽  
Interactive Machine Learning ◽  
Learning Techniques ◽  
Comprehensive Comparison ◽  
Image Caption Generation ◽  
Image Caption ◽  
Made In

Image caption generation is a stimulating multimodal task. Substantial advancements have been made in thefield of deep learning notably in computer vision and natural language processing. Yet, human-generated captions are still considered better, which makes it a challenging application for interactive machine learning. In this paper, we aim to compare different transfer learning techniques and develop a novel architecture to improve image captioning accuracy. We compute image feature vectors using different state-of-the-art transferlearning models which are fed into an Encoder-Decoder network based on Stacked LSTMs with soft attention,along with embedded text to generate high accuracy captions. We have compared these models on severalbenchmark datasets based on different evaluation metrics like BLEU and METEOR.

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