Abstract
Every day, websites and personal archives generate an increasing number of photographs. The extent of these archives is unfathomable. The ease of usage of these enormous digital image collections contributes to their popularity. However, not all of these databases provide appropriate indexing data. As a result, it’s tough to find information that the user is interested in. Thus, in order to find information about an image, it is necessary to classify its content in a meaningful way. Image annotation is one of the most difficult issues in computer vision and multimedia research. The objective is to convert an image into a single or numerous labels. This necessitates a grasp of the visual content of an image. The necessity for unambiguous information to build semantic-level concepts from raw image pixels is one of the challenges of image annotation. Unlike text annotation, where a dictionary links words to their meaning, raw picture pixels are insufficient to construct semantic-level notions directly. A simple syntax, on the other hand, is well specified for combining letters to form words and words to form sentences. The automatic feature extraction for automatic annotation was the emphasis of this paper. And they employed a deep learning convolutional neural network to build and improve image coding and annotation capabilities. Performance of the suggested technique on the Corel-5K, ESP-Game, and IAPRTC-12 datasets. Finally, experimental findings on three data sets were used to demonstrate the usefulness of this model for image annotation.
Highlights
In recent years, it has become very difficult to search an image in a large image database
The highest N+ is provided by the convolutional neural network (CNN)-SLantlet Transform (SLT) method with three-pass k-nearest neighbor’s algorithm (KNN), with a value of 260, which is greater than the highest 259 in the other nine analyzed algorithms, and an improvement of at least 3 over the compared algorithms
The R value for the CNN-SLT and 2PKNN technique is 0.41, which is higher than the R value for the other algorithms
Summary
It has become very difficult to search an image in a large image database. CBIR (Content-Based Image Retrieval) is a technique for recovering images from lowlevel visual attributes Another way for overcoming the issues of CBIR systems is to assign labels to all photos in the database. The key advantage of this method is that the image can be retrieved in the same way that a text document can be retrieved This label assignment method is called image annotation. 3 - Based on our proposed as some image annotation models require considerable computation time and complexity during the training phase, they become computationally intensive when training datasets are large.
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