Consistent image processing based on co‐saliency
Abstract In a group of images, the recurrent foreground objects are considered as the key objects in the group of images. In co‐saliency detection, these are described as common saliency objects. The aim is to be able to naturally guide the user's gaze to these common salient objects. By guidin...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-09-01
|
| Series: | CAAI Transactions on Intelligence Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cit2.12020 |
| _version_ | 1797990711432314880 |
|---|---|
| author | Xiangnan Ren Jinjiang Li Zhen Hua Xinbo Jiang |
| author_facet | Xiangnan Ren Jinjiang Li Zhen Hua Xinbo Jiang |
| author_sort | Xiangnan Ren |
| collection | DOAJ |
| description | Abstract In a group of images, the recurrent foreground objects are considered as the key objects in the group of images. In co‐saliency detection, these are described as common saliency objects. The aim is to be able to naturally guide the user's gaze to these common salient objects. By guiding the user's gaze, users can easily find these common saliency objects without interference from other information. Therefore, a method is proposed for reducing user visual attention based on co‐saliency detection. Through the co‐saliency detection algorithm and matting algorithm for image preprocessing, the exact position of non‐common saliency objects (called Region of Interest here, i.e. ROI) in the image group can be obtained. In the attention retargeting algorithm, the internal features of the image to adjust the saliency of the ROI areas are considered. In the HSI colour space, the three components H, S, and I are adjusted separately. First, the hue distribution is constructed by the Dirac kernel function, and then the most similar hue distribution to the surrounding environment is selected as the best hue distribution of ROI areas. The S and I components can be set as the contrast difference between ROI areas and surrounding background areas according to the user's demands. Experimental results show that this method effectively reduces the ROI areas' attraction to the user's visual attention. Moreover, comparing this method with other methods, the saliency adjustment effect achieved is much better, and the processed image is more natural. |
| first_indexed | 2024-04-11T08:41:06Z |
| format | Article |
| id | doaj.art-6c7790a2427b4f65b9d234da8ab937c4 |
| institution | Directory Open Access Journal |
| issn | 2468-2322 |
| language | English |
| last_indexed | 2024-04-11T08:41:06Z |
| publishDate | 2021-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | CAAI Transactions on Intelligence Technology |
| spelling | doaj.art-6c7790a2427b4f65b9d234da8ab937c42022-12-22T04:34:12ZengWileyCAAI Transactions on Intelligence Technology2468-23222021-09-016332433710.1049/cit2.12020Consistent image processing based on co‐saliencyXiangnan Ren0Jinjiang Li1Zhen Hua2Xinbo Jiang3School of Computer Science and Technology Shandong Technology and Business University Yantai ChinaSchool of Computer Science and Technology Shandong Technology and Business University Yantai ChinaSchool of Information and Electronic Engineering Shandong Technology and Business University Yantai ChinaSchool of Computer Science and Technology Shandong Technology and Business University Yantai ChinaAbstract In a group of images, the recurrent foreground objects are considered as the key objects in the group of images. In co‐saliency detection, these are described as common saliency objects. The aim is to be able to naturally guide the user's gaze to these common salient objects. By guiding the user's gaze, users can easily find these common saliency objects without interference from other information. Therefore, a method is proposed for reducing user visual attention based on co‐saliency detection. Through the co‐saliency detection algorithm and matting algorithm for image preprocessing, the exact position of non‐common saliency objects (called Region of Interest here, i.e. ROI) in the image group can be obtained. In the attention retargeting algorithm, the internal features of the image to adjust the saliency of the ROI areas are considered. In the HSI colour space, the three components H, S, and I are adjusted separately. First, the hue distribution is constructed by the Dirac kernel function, and then the most similar hue distribution to the surrounding environment is selected as the best hue distribution of ROI areas. The S and I components can be set as the contrast difference between ROI areas and surrounding background areas according to the user's demands. Experimental results show that this method effectively reduces the ROI areas' attraction to the user's visual attention. Moreover, comparing this method with other methods, the saliency adjustment effect achieved is much better, and the processed image is more natural.https://doi.org/10.1049/cit2.12020feature extractionimage colour analysisimage segmentationobject detection |
| spellingShingle | Xiangnan Ren Jinjiang Li Zhen Hua Xinbo Jiang Consistent image processing based on co‐saliency CAAI Transactions on Intelligence Technology feature extraction image colour analysis image segmentation object detection |
| title | Consistent image processing based on co‐saliency |
| title_full | Consistent image processing based on co‐saliency |
| title_fullStr | Consistent image processing based on co‐saliency |
| title_full_unstemmed | Consistent image processing based on co‐saliency |
| title_short | Consistent image processing based on co‐saliency |
| title_sort | consistent image processing based on co saliency |
| topic | feature extraction image colour analysis image segmentation object detection |
| url | https://doi.org/10.1049/cit2.12020 |
| work_keys_str_mv | AT xiangnanren consistentimageprocessingbasedoncosaliency AT jinjiangli consistentimageprocessingbasedoncosaliency AT zhenhua consistentimageprocessingbasedoncosaliency AT xinbojiang consistentimageprocessingbasedoncosaliency |