This weighting indirectly enhances the performance of image retrieval (because search keywords often reflect the users’ interest only in the salient regions) of the image. During annotation, salient regions of a image are weighted to give them higher priority. During pre-processing, the image is divided into salient and non-salient regions. In this paper, visual attention mechanism is introduced into the annotation process. Other annotation algorithms do not segment the image at all but extract the whole image as a feature. Most existing automatic image annotation algorithms do not consider the relative importance of different parts of the image to the user when segmenting the image. Segmenting image is very important for automatic image annotation, greatly influencing the annotation results. As long as the image can be accurately annotated, good results can be returned to the user. Automatic image annotation has become an important research topic in the field of image retrieval. That is, users need to provide only the query keywords, with the system returning the images associated with the keywords, which is in line with most users’ current habits. Image retrieval process can be indirectly changed into text retrieval by automatic image annotation. In comparison, content-based image retrieval (CBIR) increases the amount of effort from users, often requiring them to provide initial images as input for the search. The dramatic increase in the number of images on the Internet combined with the subjectivity, uncertainty and laboriousness of manual annotation has led to a gradual failure to apply text-based image retrieval (TBIR) to large-scale image retrieval. It is also becoming harder and harder for users to find these images accurately and quickly. With the development of digital and Internet technologies, there are massive numbers of digital images on the Internet. Experimental results show the effectiveness of the proposed algorithm. The support vector machine uses particle swarm optimization to annotate the images automatically. When the image is annotated, words relating to the salient region are given first. A preprocessing step divides the image into two parts, the salient regions and everything else, and the annotation step places a greater weight on the salient region. We propose an algorithm that integrates a visual attention mechanism with image annotation. Users searching for images are usually only interested in the salient areas. This makes it difficult for annotation words to reflect salient versus non-salient areas of the image. Currently most automatic image annotation algorithms do not consider the relative importance of each region in the image, and some algorithms extract the image features as a whole. Users of annotated images can locate images they want to search by providing keywords. Automatic image annotation not only has the efficiency of text-based image retrieval but also achieves the accuracy of content-based image retrieval.
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