Adaptive Coati Optimization Enabled Deep CNN-based Image Captioning
The task of providing a natural language description of graphical information of the image is known as image captioning. As a result, it needs an algorithm to create a series of output words and understand the relations between textual and visual elements. The main goal of this research is to captio...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2381166 |
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| author | Balasubramaniam S Seifedine Kadry Rajesh Kumar Dhanaraj Satheesh Kumar K |
| author_facet | Balasubramaniam S Seifedine Kadry Rajesh Kumar Dhanaraj Satheesh Kumar K |
| author_sort | Balasubramaniam S |
| collection | DOAJ |
| description | The task of providing a natural language description of graphical information of the image is known as image captioning. As a result, it needs an algorithm to create a series of output words and understand the relations between textual and visual elements. The main goal of this research is to caption the image by extracting the features and detecting the object from the image. Here, the object is detected by employing Deep Embedding Clustering. The features from the input image are extracted such as Local Vector Pattern (LVP), Spider Local Image Features, and some statistical features like mean, variance, standard deviation, kurtosis, and skewness. The extracted features and detected objects are given to image captioning which is exploited by Deep Convolutional Neural Network (Deep CNN). The Deep CNN is trained by using the proposed Adaptive Coati Optimization Algorithm (ACOA). The proposed ACOA is attained by the integration of the Adaptive concept and Coati Optimization Algorithm (COA) and thus the image is captioned. The proposed ACOA achieved maximum values in the training data such as 90.5% of precision, 89.9% of recall 89.1% of F1-Score, 90.4% of accuracy, 90.4% of BELU, and 90.9% of ROUGE. |
| format | Article |
| id | doaj-art-e2005671e17e4f73bde73abc903fb909 |
| institution | OA Journals |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-e2005671e17e4f73bde73abc903fb9092025-08-20T02:36:22ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452024-12-0138110.1080/08839514.2024.2381166Adaptive Coati Optimization Enabled Deep CNN-based Image CaptioningBalasubramaniam S0Seifedine Kadry1Rajesh Kumar Dhanaraj2Satheesh Kumar K3School of Computer Science and Engineering, Kerala University of Digital Sciences, Innovation and Technology (Formerly IIITM-K), Digital University Kerala, Thiruvananthapuram, IndiaDepartment of Applied Data Science, Noroff University College, Kristiansand, NorwaySymbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Pune, IndiaDepartment of Futures Studies, University of Kerala, Thiruvananthapuram, IndiaThe task of providing a natural language description of graphical information of the image is known as image captioning. As a result, it needs an algorithm to create a series of output words and understand the relations between textual and visual elements. The main goal of this research is to caption the image by extracting the features and detecting the object from the image. Here, the object is detected by employing Deep Embedding Clustering. The features from the input image are extracted such as Local Vector Pattern (LVP), Spider Local Image Features, and some statistical features like mean, variance, standard deviation, kurtosis, and skewness. The extracted features and detected objects are given to image captioning which is exploited by Deep Convolutional Neural Network (Deep CNN). The Deep CNN is trained by using the proposed Adaptive Coati Optimization Algorithm (ACOA). The proposed ACOA is attained by the integration of the Adaptive concept and Coati Optimization Algorithm (COA) and thus the image is captioned. The proposed ACOA achieved maximum values in the training data such as 90.5% of precision, 89.9% of recall 89.1% of F1-Score, 90.4% of accuracy, 90.4% of BELU, and 90.9% of ROUGE.https://www.tandfonline.com/doi/10.1080/08839514.2024.2381166 |
| spellingShingle | Balasubramaniam S Seifedine Kadry Rajesh Kumar Dhanaraj Satheesh Kumar K Adaptive Coati Optimization Enabled Deep CNN-based Image Captioning Applied Artificial Intelligence |
| title | Adaptive Coati Optimization Enabled Deep CNN-based Image Captioning |
| title_full | Adaptive Coati Optimization Enabled Deep CNN-based Image Captioning |
| title_fullStr | Adaptive Coati Optimization Enabled Deep CNN-based Image Captioning |
| title_full_unstemmed | Adaptive Coati Optimization Enabled Deep CNN-based Image Captioning |
| title_short | Adaptive Coati Optimization Enabled Deep CNN-based Image Captioning |
| title_sort | adaptive coati optimization enabled deep cnn based image captioning |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2024.2381166 |
| work_keys_str_mv | AT balasubramaniams adaptivecoatioptimizationenableddeepcnnbasedimagecaptioning AT seifedinekadry adaptivecoatioptimizationenableddeepcnnbasedimagecaptioning AT rajeshkumardhanaraj adaptivecoatioptimizationenableddeepcnnbasedimagecaptioning AT satheeshkumark adaptivecoatioptimizationenableddeepcnnbasedimagecaptioning |