Fig. 1. Flow chart of the proposed algorithm

Fig. 2. Schematic of region segmentation based on multi-feature fusion clustering

Fig. 3. Schematic of superpixel merging strategy

Fig. 4. Schematic of matching strategy

Fig. 5. Schematic of sparse disparity construction. (a) Matching point pairs; (b) match points in the merged regions; (c) sparse disparity; (d) stereo image of sparse disparity

Fig. 6. Limitation of depth saliency detection

Fig. 7. Comparison of saliency map fusion and background suppression results. (a) Original left image; (b) sparse disparity map; (c) FT global saliency map; (d) FT regional mean saliency map; (e) fusing saliency map; (f) saliency map of results after background interference

Fig. 8. Operation interface of simulation software

Fig. 9. Comparison of saliency maps generated by the proposed algorithm and global contrast algorithms. (a) Original left image; (b) original right image; (c) GT algorithm; (d) proposed algorithm; (e) LC algorithm; (f) FT algorithm; (g) HC algorithm; (h) PCA algorithm

Fig. 10. Comparison of saliency maps generated by the proposed algorithm and local contrast algorithms. (a) Original left image; (b) original right image; (c) GT algorithm; (d) proposed algorithm; (e) AC algorithm; (f) CA algorithm; (g) SEG algorithm

Fig. 11. Comparison of saliency maps generated by the proposed algorithm and prior information algorithms. (a) Original left image; (b) original right image; (c) GT algorithm; (d) proposed algorithm; (e) DSR algorithm; (f) GR algorithm; (g) RBD algorithm; (h) LPS algorithm; (i) MILPS algorithm

Fig. 12. P-R curves of the proposed method and other algorithms. (a) Comparison with the global contrast algorithm; (b) comparison with the local contrast algorithm; (c) comparison with the prior information algorithm

Fig. 13. MAE, AUC, F value histograms of the proposed method and other algorithms. (a) Comparison with the global contrast algorithm; (b) comparison with the local contrast algorithm; (c) comparison with the prior information algorithm