Image Captioning Using Deep Learning

Kanimozhiselvi, C. S.; Karthika, None V.; Kalaivani, S P; Krithika, S

doi:10.1109/iccci54379.2022.9740788

Cited by 9 publications

(2 citation statements)

References 13 publications

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“…Kanimozhiselvi et al [11] proposed an Image Captioning model that combines CNN architecture with LSTM to improve results. The model employs three distinct CNN and LSTM architectures and is training utilizing three CNN architectures (InceptionV3, Xception, and ResNet50) for extraction of visual characteristics and the LSTM for caption creation.…”

Section: Sharma Et Almentioning

confidence: 99%

Next-LSTM: a novel LSTM-based image captioning technique

Singh

Kumar

2023

Int J Syst Assur Eng Manag

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Recently, Image Captioning has evolved into an immensely popular area in the eld of Computer Vision. It strives to generate natural language sentences in order to describe the salient parts of a given image. Research in this area is active and various Machine learning-based Image Captioning models have been proposed in the literature. The main challenge with the existing approaches is effectively extracting image features to generate adequate image captions. Further, there is a need to improve the generalizability of the results on large and diverse datasets. In the current paper, a novel method framework, namely Next-LSTM is proposed for Image Captioning. It rst extracts the image features using ResNeXt. It is a powerful convolution neural network-based model that is adopted for the rst time in the Image Captioning domain. Later, it applies a Long-short term memory network on the extracted features to generate accurate captions for the images. The proposed framework is then evaluated on the benchmark Flickr-8k dataset on Accuracy. The performance of the proposed framework is also compared to the state-of-the-art approaches and it outperforms the existing approaches.

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Section: Sharma Et Almentioning

confidence: 99%

Next-LSTM: a novel LSTM-based image captioning technique

Singh

Kumar

2023

Int J Syst Assur Eng Manag

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“…This paper introduces an automatic image captioning framework that generates semantically meaningful captions. The approach uses a deep neural network architecture, comprising a CNN that encodes the visual features and RNN that decodes and generates the text [7], [8]. It then employs LSTM [9], [10] and gated recurrent units (GRU) [11] to derive significant insights.…”

Section: Introductionmentioning

confidence: 99%

Automatic Generation of Image Caption Based on Semantic Relation using Deep Visual Attention Prediction

EL-GAYAR

2023

IJACSA

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While modern systems for managing, retrieving, and analyzing images heavily rely on deriving semantic captions to categorize images, this task presents a considerable challenge due to the extensive capabilities required for manual processing, particularly with large images. Despite significant advancements in automatic image caption generation and human attention prediction through convolutional neural networks, there remains a need to enhance attention models in these networks through efficient multi-scale features utilization. Addressing this need, our study presents a novel image decoding model that integrates a wavelet-driven convolutional neural network with a dual-stage discrete wavelet transform, enabling the extraction of salient features within images. We utilize a wavelet-driven convolutional neural network as the encoder, coupled with a deep visual prediction model and Long Short-Term Memory as the decoder. The deep Visual Prediction Model calculates channel and location attention for visual attention features, with local features assessed by considering the spatial-contextual relationship among objects. Our primary contribution is to propose an encoder and decoder model to automatically create a semantic caption on the image based on the semantic contextual information and spatial features present in the image. Also, we improved the performance of this model, demonstrated through experiments conducted on three widely used datasets: Flickr8K, Flickr30K, and MSCOCO. The proposed approach outperformed current methods, achieving superior results in BLEU, METEOR, and GLEU scores. This research offers a significant advancement in image captioning and attention prediction models, presenting a promising direction for future work in this field.

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