An Improved Automatic Image Annotation Approach Using Convolutional Neural Network-Slantlet Transform

Adnan, Myasar Mundher; Rahim, Mohd Shafry Mohd; Khan, Ali R.; Saba, Tanzila; Fati, Suliman Mohamed; Bahaj, Saeed Ali

doi:10.1109/access.2022.3140861

Cited by 14 publications

(11 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, the ResNet50-SLT method had the highest F1 score at 81%. Furthermore, the ResNet50-SLT method produced the highest N+ with CNN-THOP (240) compared to the one attained by other algorithms (290) [46,47], which was improved by at least 4. Additionally, The F1 scores of ResNet50-SLT show a 15% difference between its performance and that of CNN-THOP, indicating that ResNet50-SLT performed better.…”

Section: D-comparative Performance Evaluation Of the Proposed Methods...mentioning

confidence: 97%

See 1 more Smart Citation

Automated Image Annotation With Novel Features Based on Deep ResNet50-SLT

et al. 2023

View full text Add to dashboard Cite

Due to their vast size, the growing number of digital images found in personal archives and on websites has become unmanageable, making it challenging to accurately retrieve images from these large databases. While these collections are popular due to their convenience, they are often not equipped with proper indexing information, making it difficult for users to find what they need. One of the most significant challenges in the field of computer vision and multimedia is image annotation, which involves labeling images with descriptive keywords. However, computers do not possess the capability to understand the essence of images in the same way that humans do, and people can only identify images based on their visual attributes, not their deeper semantic meaning. Therefore, image annotation requires the use of keywords to effectively communicate the contents of an image to a computer system. However, raw pixels in an image do not provide enough information to generate semantic concepts, making image annotation a complex task. Unlike text annotation, where the dictionary linking words to semantics is well established, image annotation lacks a clear definition of "words" or "sentences" that can be associated with the meaning of the image, known as the semantic gap. To address this challenge, this study aimed to characterize image content meaningfully to make information retrieval easier. An improved automatic image annotation (AIA) system was proposed to bridge the semantic gap between low-level computer features and human interpretation of images by assigning one or multiple labels to images. The proposed AIA system can convert raw image pixels into semantic-level concepts, providing a clearer representation of the image content. The study combined the ResNet50 and slantlet transform with word2vec and principal component analysis with t-distributed stochastic neighbor embedding to balance precision and recall. This allowed the researchers to determine the optimal model for the proposed ResNet50-SLT AIA framework. A Word2vec model with ResNet50-SLT was used with principal component analysis and t-distributed stochastic neighbor embedding to improve IA prediction accuracy. The distributed representation approach involved encoding and storing information about image features. The proposed AIA system utilized seq2seq to generate sentences depending on feature vectors. The system was implemented on the most popular datasets (Flickr8k, Corel-5k, ESP-Game). The results showed that the newly developed AIA scheme overcame the computational time complexity associated with most existing image annotation models during the training phase for large datasets. The performance evaluation of the AIA scheme showed its excellent flexibility of annotation, improved accuracy, and reduced computational costs, thus outperforming the existing state-ofthe-art methods. In conclusion, this AIA framework can provide immense benefits in accurately selecting and extracting image features and easily retrieving images from large databases. The ex...

show abstract

Section: D-comparative Performance Evaluation Of the Proposed Methods...mentioning

confidence: 97%

“…In the proposed method, matrix multiplication will be utilized to calculate the SLT coefficients for image blocks. The matrix (S) will be divided into four sub-bands (LL, HL, LH, and HH) based on the SLT coefficients obtained from the matrix multiplication process [28].…”

Section: S = Smentioning

confidence: 99%

Automated Image Annotation With Novel Features Based on Deep ResNet50-SLT

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Adnan et al, [11] provided an automated image annotation approach using the Convolutional Neural Network-Slantlet Transform. Its purpose is towards convert an image into single or multiple labels.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Image Annotation Using Adaptive Convolutional Deep Learning Model

Jayaraj¹,

Lokesh²

2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

Every day, websites and personal archives create more and more photos. The size of these archives is immeasurable. The comfort of use of these huge digital image gatherings donates to their admiration. However, not all of these folders deliver relevant indexing information. From the outcomes, it is difficult to discover data that the user can be absorbed in. Therefore, in order to determine the significance of the data, it is important to identify the contents in an informative manner. Image annotation can be one of the greatest problematic domains in multimedia research and computer vision. Hence, in this paper, Adaptive Convolutional Deep Learning Model (ACDLM) is developed for automatic image annotation. Initially, the databases are collected from the open-source system which consists of some labelled images (for training phase) and some unlabeled images {Corel 5 K, MSRC v2}. After that, the images are sent to the pre-processing step such as colour space quantization and texture color class map. The pre-processed images are sent to the segmentation approach for efficient labelling technique using J-image segmentation (JSEG). The final step is an automatic annotation using ACDLM which is a combination of Convolutional Neural Network (CNN) and Honey Badger Algorithm (HBA). Based on the proposed classifier, the unlabeled images are labelled. The proposed methodology is implemented in MATLAB and performance is evaluated by performance metrics such as accuracy, precision, recall and F1_Measure. With the assistance of the proposed methodology, the unlabeled images are labelled.

show abstract

“…Moreover, the constrained focus on the affected target omits essential boundary features, compounding the intricacy of achieving comprehensive segmentation, particularly for damaged tissue [8]. However, a profound understanding of learning techniques related to medical image processing and computer vision is essential for researchers and clinicians [9], [10], [11]. Medical image processing, notably image segmentation, is gaining prominence for informed patient evaluation and treatment planning.…”

Section: Medical Research Heavily Relies On Medical Image Analysis a ...mentioning

confidence: 99%

“…These characteristics are exhibited by gamma radiation, which has a frequency greater than 1019 hertz and a wavelength less than ten picometers. MRI and Ultrasound imaging methods are founded on nonionizing radiation principles; X-rays, SPECT, CT, and PET rely on ionizing radiation, as evidenced by prior research [9].…”

Section: A Medical Imaging Modalitiesmentioning

confidence: 99%

Recent Advancements and Future Prospects in Active Deep Learning for Medical Image Segmentation and Classification

Mahmood,

Rehman,

Saba

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Medical images are helpful for the diagnosis, treatment, and evaluation of diseases. Precise medical image segmentation improves diagnosis and decision-making, aiding intelligent medical services for better disease management and recovery. Due to the unique nature of medical images, image segmentation algorithms based on deep learning face problems such as sample imbalance, edge blur, false positives, and false negatives. In view of these problems, researchers primarily improve the network structure but rarely improve from the unstructured aspect. The paper tackles these challenges, accentuating the limitations of deep convolutional neural network-based methods and proposing solutions to reduce annotation costs, particularly in complex images, and introduces the improvement strategies to solve the problems of sample imbalance, edge blur, false positives, and false negatives. Additionally, the article introduces the latest deep learning-based applications in medical image analysis, covering segmentation, image acquisition, enhancement, registration, and classification. Moreover, the article provides an overview of four cutting-edge deep learning models, namely convolutional neural network (CNN), deep belief network (DBN), stacked autoencoder (SAE), and recurrent neural network (RNN). The study selection involved searching benchmark academic databases, collecting relevant literature and appropriate indicator for analysis, emphasizing DL-based segmentation and classification approaches, and evaluating performance metrics. The research highlights clinicians' and scholars' obstacles in developing an efficient and accurate malignancy prognostic framework based on state-of-the-art deep-learning algorithms. Furthermore, future perspectives are explored to overcome challenges and advance the field of medical image analysis.

show abstract

An Improved Automatic Image Annotation Approach Using Convolutional Neural Network-Slantlet Transform

Cited by 14 publications

References 51 publications

Automated Image Annotation With Novel Features Based on Deep ResNet50-SLT

Automated Image Annotation With Novel Features Based on Deep ResNet50-SLT

Automatic Image Annotation Using Adaptive Convolutional Deep Learning Model

Recent Advancements and Future Prospects in Active Deep Learning for Medical Image Segmentation and Classification

Contact Info

Product

Resources

About