Visual saliency detection: a Kalman filter based approach
This work addresses the problem of accurately detecting salient regions in images for applications like computer vision and image processing, representing an incremental improvement over prior methods.
The paper tackles visual saliency detection in static images by proposing a Kalman filter-based model that identifies salient regions as visually surprising deviations from expectations, achieving superior performance compared to existing models on two benchmark datasets.
In this paper we propose a Kalman filter aided saliency detection model which is based on the conjecture that salient regions are considerably different from our "visual expectation" or they are "visually surprising" in nature. In this work, we have structured our model with an immediate objective to predict saliency in static images. However, the proposed model can be easily extended for space-time saliency prediction. Our approach was evaluated using two publicly available benchmark data sets and results have been compared with other existing saliency models. The results clearly illustrate the superior performance of the proposed model over other approaches.