Fast Saliency Detection Using Sparse Random Color Samples and Joint Upsampling

• Published in: 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) pp. 217 - 224
• Main Authors: Lie, Maiko Min Ian, Benvenutti Borba, Gustavo, Vieira Neto, Hugo, Gamba, Humberto Remigio
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2016
• Subjects: Computational modeling; Fast Global Smoother; Image color analysis; Image edge detection; Joint upsampling; Noise measurement; Runtime; Saliency detection; Smoothing methods; Visual attention; Visualization
• ISSN: 2377-5416
• DOI: 10.1109/SIBGRAPI.2016.038

The human visual system employs a mechanism of visual attention, which selects only part of the incoming information for further processing. Through this mechanism, the brain avoids overloading its limited cognitive capacities. In computer vision, this task is usually accomplished through saliency detection, which outputs the regions of an image that are distinctive with respect to its surroundings. This ability is desirable in many technological applications, such as image compression, video quality assessment and content-based image retrieval. In this paper, a saliency detection method based on color distance with sparse random samples and joint upsampling is presented. This approach computes full-resolution saliency maps with short runtime by leveraging both edge-preserving smoothing and joint upsampling capabilities of the Fast Global Smoother. The proposed method is assessed through precision-recall curves, F-measure and average runtime on the MSRA1K dataset. Results show that the method is competitive with state-of-the-art algorithms in both saliency detection accuracy and runtime.