Saliency detection using maximum symmetric surround
By Radhakrishna Achanta, Sabine Süsstrunk. 2010 IEEE International Conference on Image Processing, 2010
416 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/39b8aa0d2337f19a60f1b467a53187fa8a065336
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In Computer Vision and Pattern Recognition
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Citations in the corpus
- Leveraging stereopsis for saliency analysis (2012, methodology): “tion methods, including RC [3], CA [11], GB [14], FT [1], SR [15], and MS [2] and used these methods as well as ours to estimate saliency maps from our benchmark dataset.”
- Center-surround divergence of feature statistics for salient object detection (2011, methodology): “[12], ST [24], AC09 [1], AC10, [2], HZ08 [10], AIM [4], our approach BITS.”
- Global Contrast Based Salient Region Detection (2015, methodology): “For the other methods namely IT, AIM, IM, MSS, SEG, SeR, SUN, GB, SR, AC, CA, FT and CB, we used the authors’ implementations, while for LC, we implemented the algorithm in C++ since we failed to obtain the authors’ implementation.”
- Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery (2016, background): “…frequency-tuned (FT) [57], graph-based visual saliency (GBVS) [58], IM [59], low rank matrix recovery (LRR) [60], maximum symmetric surround (MSS) [61], rarity-based saliency (RARE) [62], salient segmentation (SEG) [63], self-resemblance (SeR) [64], spectral residual (SR) [65] and saliency using…”
- Efficient Salient Region Detection with Soft Image Abstraction (2013, methodology): “Precision measures the per- 0 0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1 Recall P re c is io n GC SF RC MSS HC SWD SEG FT CA LC GB AIM SeR AC SUN IM IT MZ SR (a) Fixed thresholding 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Precision Recall SeR IM SEG SU N SW D AIM CARC GB M Z SRLC FTAC HC IT SFMSS GC (b) Adaptive thresh”