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

图像自动标注 计算机科学 注释 图像检索 语义鸿沟 情报检索 语义学(计算机科学) 搜索引擎索引 人工智能 领域(数学) 数字图像 图像(数学) 自然语言处理 图像处理 数学 纯数学 程序设计语言
作者
Myasar Mundher Adnan,Mohd Shafry Mohd Rahim,Azmat Ullah Khan,Ahmed Alkhayyat,Faten S. Alamri,Tanzila Saba,Saeed Ali Bahaj
出处
期刊:IEEE Access [Institute of Electrical and Electronics Engineers]
卷期号:11: 40258-40277 被引量:3
标识
DOI:10.1109/access.2023.3266296
摘要

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-of-the-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 extracted features can effectively be used to represent the image, thus accelerating the annotation process and minimizing the computational complexity.

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