| Abstrakt: |
The diversity of natural images in terms of visual features is useful in saliency detection. The complementary visual features jointly improve the performance of salient object detection. In this paper, we introduce a novel region-based feature combination approach that utilizes the diversity of visual features over image regions for robust salient object detection. The proposed approach works in four steps: (i) region formation, (ii) feature extraction, (iii) region-wise weight learning and (iv) region-based feature combination. Region formation is carried out using simple linear iterative clustering (SLIC) algorithm. Then, the features are extracted using Boundary Connectivity (BC), Contrast Cluster (CC), and Minimum Directional Contrast (MDC) methods. These features are then used for learning weights vectors for each region. Our major contribution is in step four where a novel dynamic weighted feature combination method is proposed. In this step region-wise integration weights are obtained by using a nature inspirited optimization algorithm called Constrained Particle swarm optimization (CPSO). Then salient features are region-wise combined with their dynamic relevance for final saliency map. The proposed method is compared with eight state-of-the-art saliency detection methods on five public available saliency benchmark datasets namely MSRA10K, DUT-OMRON, ECSSD, PASCAL, and SED2. The experimental results demonstrate that the proposed method performs better than state-of-the-art methods in terms of Precision, Recall, F-measure and Mean Absolute Error while comparable in terms of AUC and ROC curve. [ABSTRACT FROM AUTHOR] |
Full text from SpringerLink