Detection and Recognition of Objects in Image Caption Generator System: A Deep Learning Approach

Kumar, N. K. Senthil; Vigneswari, D.; Mohan, Alkha; Kumarapu, Laxman; Yuvaraj, J.

doi:10.1109/icaccs.2019.8728516

Cited by 18 publications

(5 citation statements)

References 7 publications

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“…These experiments show that the model is frequently giving accurate descriptions for an input image. [4] Deep Learning based Automatic Image Caption Generation. V. Kesavan, V. Muley and M. Kolhekar.…”

Section: Literature Reviewmentioning

confidence: 99%

Image Caption Generator by using CNN and LSTM

-¹

2023

IJFMR

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In this article, we systematically analyze a deep neural networks-based image caption generation method. Image Captioning aims to automatically generate a sentence description for an image. Our article model will take an image as input and generate on English sentence as output, describing the contents of the image. It has attracted much research attention in cognitive computing in the recent years. The task is rather complex, as the concepts of both computer vision and natural language processing domains are combined together. We have developed a model using the concepts of a Convolutional Neural Network (CNN) and long Short-Term Memory (LSTM) model and build a working model of Image caption generator by implementing CNN and LSTM. After the caption generation phase, we use BLEU Scores to evaluate the efficiency of our model. Thus, our system helps the user to get descriptive caption for the given input image.

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Section: Literature Reviewmentioning

confidence: 99%

Image Caption Generator by using CNN and LSTM

-¹

2023

IJFMR

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“…Lebret et al [18] proposed a simple language model based on caption syntax statistics to produce appropriate captions for an identified test image with the phrases deduced. N. K. Kumar et al [11] proposed Regional Object Detector (RODe) to detect, recognize, and generate descriptions that aim at deep learning to still enhance on top of the prevailing image description generator systems. Kinghorn et al [19] proposed a region-based deep learning architecture in image caption generation by using a regional object detector, recurrent neural network (RNN)-based attribute prediction, and an encoder-decoder language generator embedded with two RNNs to generate processed and thorough captions for an identified image.…”

Section: Related Workmentioning

confidence: 99%

“…A further complicated graph of recognition behind triads is shown by Kulkarni et al [10]. Deep learning architecture was used for image caption generation in [11]. In [12], [13], the authors used Convolutional Neural Network (CNN) in combination with Long Short-Term Memory (LSTM) to produce image descriptions but fail to achieve promising accuracy.…”

Section: Introductionmentioning

confidence: 99%

Image Description Generator using Residual Neural Network and Long Short-Term Memory

Morampudi¹,

Gonthina

Bhaskar

et al. 2023

CSJM

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Human beings can describe scenarios and objects in a picture through vision easily whereas performing the same task with a computer is a complicated one. Generating captions for the objects of an image helps everyone to understand the scenario of the image in a better way. Instinctively describing the content of an image requires the apprehension of computer vision as well as natural language processing. This task has gained huge popularity in the field of technology and there is a lot of research work being carried out. Recent works have been successful in identifying objects in the image but are facing many challenges in generating captions to the given image accurately by understanding the scenario. To address this challenge, we propose a model to generate the caption for an image. Residual Neural Network (ResNet) is used to extract the features from an image. These features are converted into a vector of size 2048. The caption generation for the image is obtained with Long Short-Term Memory (LSTM). The proposed model is experimented on the Flickr8K dataset and obtained an accuracy of 88.4\%. The experimental results indicate that our model produces appropriate captions compared to the state of art models.

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“…Related work [1] Deep Learning is used to detection and recognition of objects in image. Convolution Neural Network (CNN) approach is used for feature extraction.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection and Recognition of Object for Image Captioning

Mani¹,

Vishwakarma²,

Tiwari³

2021

AJCT

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As one of the most intelligent beings on the planet, we are equipped with the most powerful visual and language system as it is easy for us to extract visual information from a given image and transform it into proper linguistic description. Image Caption Generator deals with generating captions for a given image. The capturing mechanism involves a tiring task that collaborates both computer vision and image processing. The mechanism must detect and establish relationships between objects, people, and animals. The aim of this paper is to detect, recognize and generate worthwhile captions for a given image. We use transfer learning CNN on sentences, and extract image representation with Neural Networks.

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Detection and Recognition of Objects in Image Caption Generator System: A Deep Learning Approach

Cited by 18 publications

References 7 publications

Image Caption Generator by using CNN and LSTM

Image Caption Generator by using CNN and LSTM

Image Description Generator using Residual Neural Network and Long Short-Term Memory

Detection and Recognition of Object for Image Captioning

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