Multi-Scale Saliency Detection using Dictionary Learning
This work addresses saliency detection for applications in computer vision and computational photography, but it appears incremental as it builds on existing dictionary learning techniques.
The paper tackles the problem of saliency detection in images by proposing a method using multimodal dictionary learning, which improves performance over non-task-specific approaches.
Saliency detection has drawn a lot of attention of researchers in various fields over the past several years. Saliency is the perceptual quality that makes an object, person to draw the attention of humans at the very sight. Salient object detection in an image has been used centrally in many computational photography and computer vision applications like video compression, object recognition and classification, object segmentation, adaptive content delivery, motion detection, content aware resizing, camouflage images and change blindness images to name a few. We propose a method to detect saliency in the objects using multimodal dictionary learning which has been recently used in classification and image fusion. The multimodal dictionary that we are learning is task driven which gives improved performance over its counterpart (the one which is not task specific).