| Paper ID | IVMSP-26.4 |
| Paper Title |
AN ATTENTION BASED WAVELET CONVOLUTIONAL MODEL FOR VISUAL SALIENCY DETECTION |
| Authors |
Reshmi Bhooshan, College of Engineering, Trivandrum, India; Suresh K., Govt. Engineering College, Barton Hill, India |
| Session | IVMSP-26: Attention for Vision |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Image, Video, and Multidimensional Signal Processing: [IVTEC] Image & Video Processing Techniques |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
The emergence of deep neural architectures greatly enhanced the accuracy of salient region detection algorithms that plays a vital role in computer vision applications. However, the accurate extraction of regions with fine boundaries still remains as a challenge. In this work, an attention based Wavelet Convolutional Neural Network (WCNN) is implemented that efficiently extracts the spatial, spectral and semantic features of the image in multiple resolution and it turns out to be suitable for locating the visually salient regions. Further enhancement of the fine boundaries of the predicted map is made possible by the inclusion of a combinational loss function of balanced cross entropy loss, SSIM loss and edge loss. The effectiveness of the method is evaluated using three benchmark datasets and the results shows better performance achieving a minimum Mean Absolute Error (MAE) of 0.032 and maximum F-measure of 0.938. |
