Image Annotation With YCbCr Color Features Based on Multiple Deep CNN- GLP
Digital image collections are becoming increasingly popular due to their ease of use. Still, the need for adequate indexing information makes it difficult for users to find the specific images they need. With the vast number of digital images generated daily, these databases have become enormous, ma...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10310189/ |
| _version_ | 1797346291328483328 |
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| author | Myasar Mundher Adnan Waleed Hadi Madhloom Kurdi Sarah Alotaibi Amjad Rehman Saeed Ali Omer Bahaj Mohammed Hasan Ali Tanzila Saba |
| author_facet | Myasar Mundher Adnan Waleed Hadi Madhloom Kurdi Sarah Alotaibi Amjad Rehman Saeed Ali Omer Bahaj Mohammed Hasan Ali Tanzila Saba |
| author_sort | Myasar Mundher Adnan |
| collection | DOAJ |
| description | Digital image collections are becoming increasingly popular due to their ease of use. Still, the need for adequate indexing information makes it difficult for users to find the specific images they need. With the vast number of digital images generated daily, these databases have become enormous, making accurate image retrieval challenging. One of the most challenging tasks in computer vision and multimedia research is image annotation, where keywords are assigned to an image. Unlike humans, computers can measure colors, textures, and shapes of images but fail to interpret them semantically, known as the semantic gap. This makes image annotation complex. For semantic-level concepts generation the raw image pixels provide not enough Unmistakable information. Which mean for of “words” or “sentences” there is no clear definition with the semantics of an image unlike text annotation. Therefore, this study aims to bridge the semantic gap between low-level computer features and human interpretation of images. The proposed enhanced automatic image annotation system maps multiple labels or into single image, providing an in-depth understanding of the visual content’s meaning. This is achieved by combining Convolutional Neural Networks-based multiple features (Y is the green component of the color, Cb and Cr is the blue component and red component called YCbCr color space and Gaussian–Laplacian Pyramid) and neighbors to recall and balance precision. The image annotation (IA) scheme uses a Global Vectors for Word Representation (GloVe) model with CNN-Gaussian–Laplacian Pyramid and learning representation to predict image annotation (IA) accurately. The proposed image annotation (IA) system was execution on three public datasets and showed excellent flexibility of annotation, improved accuracy, and reduced computational costs compared to existing state-of-the-art methods. The image annotation (IA) framework can provide immense benefits in accurately selecting and extracting image features, minimizing computational complexity and facilitating annotation. |
| first_indexed | 2024-03-08T11:30:55Z |
| format | Article |
| id | doaj.art-465b1752233a44b8a5b57e0b00b93fe0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T11:30:55Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-465b1752233a44b8a5b57e0b00b93fe02024-01-26T00:01:38ZengIEEEIEEE Access2169-35362024-01-0112113401135310.1109/ACCESS.2023.333076510310189Image Annotation With YCbCr Color Features Based on Multiple Deep CNN- GLPMyasar Mundher Adnan0https://orcid.org/0000-0003-3260-9171Waleed Hadi Madhloom Kurdi1Sarah Alotaibi2https://orcid.org/0000-0002-7195-7999Amjad Rehman3https://orcid.org/0000-0002-3817-2655Saeed Ali Omer Bahaj4https://orcid.org/0000-0003-3406-4320Mohammed Hasan Ali5https://orcid.org/0000-0001-7963-0918Tanzila Saba6https://orcid.org/0000-0003-3138-3801Faculty of Engineering, School of Computing, Universiti Teknologi Malaysia, Skudai, MalaysiaNursing Department, Altoosi University College, Najaf, IraqDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaCCIS, Artificial Intelligence & Data Analytics Laboratory, Prince Sultan University, Riyadh, Saudi ArabiaMIS Department, College of Business Administration, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaCollege of Technical Engineering, Imam Ja’afar Al-Sadiq University, Al-Muthanna, IraqCCIS, Artificial Intelligence & Data Analytics Laboratory, Prince Sultan University, Riyadh, Saudi ArabiaDigital image collections are becoming increasingly popular due to their ease of use. Still, the need for adequate indexing information makes it difficult for users to find the specific images they need. With the vast number of digital images generated daily, these databases have become enormous, making accurate image retrieval challenging. One of the most challenging tasks in computer vision and multimedia research is image annotation, where keywords are assigned to an image. Unlike humans, computers can measure colors, textures, and shapes of images but fail to interpret them semantically, known as the semantic gap. This makes image annotation complex. For semantic-level concepts generation the raw image pixels provide not enough Unmistakable information. Which mean for of “words” or “sentences” there is no clear definition with the semantics of an image unlike text annotation. Therefore, this study aims to bridge the semantic gap between low-level computer features and human interpretation of images. The proposed enhanced automatic image annotation system maps multiple labels or into single image, providing an in-depth understanding of the visual content’s meaning. This is achieved by combining Convolutional Neural Networks-based multiple features (Y is the green component of the color, Cb and Cr is the blue component and red component called YCbCr color space and Gaussian–Laplacian Pyramid) and neighbors to recall and balance precision. The image annotation (IA) scheme uses a Global Vectors for Word Representation (GloVe) model with CNN-Gaussian–Laplacian Pyramid and learning representation to predict image annotation (IA) accurately. The proposed image annotation (IA) system was execution on three public datasets and showed excellent flexibility of annotation, improved accuracy, and reduced computational costs compared to existing state-of-the-art methods. The image annotation (IA) framework can provide immense benefits in accurately selecting and extracting image features, minimizing computational complexity and facilitating annotation.https://ieeexplore.ieee.org/document/10310189/Features extractionYCbCr colordigital learningGaussian–Laplacian pyramidimage annotationtechnological development |
| spellingShingle | Myasar Mundher Adnan Waleed Hadi Madhloom Kurdi Sarah Alotaibi Amjad Rehman Saeed Ali Omer Bahaj Mohammed Hasan Ali Tanzila Saba Image Annotation With YCbCr Color Features Based on Multiple Deep CNN- GLP IEEE Access Features extraction YCbCr color digital learning Gaussian–Laplacian pyramid image annotation technological development |
| title | Image Annotation With YCbCr Color Features Based on Multiple Deep CNN- GLP |
| title_full | Image Annotation With YCbCr Color Features Based on Multiple Deep CNN- GLP |
| title_fullStr | Image Annotation With YCbCr Color Features Based on Multiple Deep CNN- GLP |
| title_full_unstemmed | Image Annotation With YCbCr Color Features Based on Multiple Deep CNN- GLP |
| title_short | Image Annotation With YCbCr Color Features Based on Multiple Deep CNN- GLP |
| title_sort | image annotation with ycbcr color features based on multiple deep cnn glp |
| topic | Features extraction YCbCr color digital learning Gaussian–Laplacian pyramid image annotation technological development |
| url | https://ieeexplore.ieee.org/document/10310189/ |
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