Salient Region Detection and Segmentation

By Radhakrishna Achanta, Francisco J. Estrada, Patricia Wils, Sabine Süsstrunk. International Conference on Virtual Storytelling, 2008

976 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b7a3ea33da5e3c848c31ffce96be7d905131218c

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In Computer Vision and Pattern Recognition

Citations in the corpus

Saliency filters: Contrast based filtering for salient region detection (2012, background): “We compare to spectral residual saliency (SR [16]), fuzzy growing (MZ [22]), spatiotemporal cues (LC [29]), visual attention measure (IT [18]), graph-based saliency (GB [15]), salient region detection (AC [1]), context-aware saliency (CA [12]), frequency-tuned saliency (FT [2]) and global-contrast s”
Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding (2014, methodology): “This paper follows an elegant algorithm proposed by Achanta et al. (2008, 2009).”
Frequency-tuned salient region detection (2009, methodology): “[1] that outputs saliency maps of the same size as the input image.”
Automatic Road Crack Detection Using Random Structured Forests (2016, methodology): “Although existing methods [33], [34] demonstrate their effectiveness in detecting salient regions in the Berkeley database [35], they perform poor on the completeness and continuity of detected crack.”
The knowing camera 2: recognizing and annotating places-of-interest in smartphone photos (2014, background): “A lot of work exist in the ﬁeld of salient region detection which aims at detecting visually salient image regions[3, 21, 13].”
Global contrast based salient region detection (2011, methodology): “We have extensively evaluated our methods on publicly available benchmark data sets, and compared our methods with (eight) state-of-the-art saliency methods [17, 21, 32, 14, 15, 1, 2, 12] as well as with manually produced ground truth annotations1.”
Saliency Detection via Graph-Based Manifold Ranking (2013, methodology): “We compare our method with fourteen state-of-the-art saliency detection algorithms: the IT [17], GB [14], MZ [25], SR [15], AC [1], Gof [11], FT [2], LC [37], RC [9], SVO [7], SF [27], CB [18], GS SP [34] and XIE [35] methods.”
What Do Different Evaluation Metrics Tell Us About Saliency Models? (2016, methodology): “Consider a detection application of saliency such as object and motion detection, surveillance, localization and mapping, and segmentation [1], [17], [27], [28], [43], [50], [59], [91].”
Global Contrast Based Salient Region Detection (2015, methodology): “tion method on three different types of benchmark datasets, and compared it against 15 alternate methods—SR[30], IT [17], IM [23], SUN [27], AC [31], SeR [26], AIM [22], GB [29], LC [34], CA [32], FT [33], SWD [28], SEG [25], MSS [24], LP [49] and CB [35], respectively.”
Efficient Salient Region Detection with Soft Image Abstraction (2013, methodology): “Results of the alternative methods are obtained by one of the following ways: i) results for this famous dataset provided by the original authors (FT[2], SF[42], AC[1], HC[16], RC[16]), ii) running the authors’ publicly available source code (GB[27], SR[28], CA[25], AIM[8], IM[39], MSS[3], SEG[43], ”
Salient object detection: A survey (2014, methodology): “nvestigations (e.g., [32]) used ﬁxations as a means to verify the saliency hypothesis and to compare models. A second wave of interest surged with the works of Liu et al. [139,140] and Achanta et al. [5] who deﬁned saliency detection as a binary segmentation problem. These authors were inspired by s”
Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery (2016, methodology): “…with [39], where only one saliency detection method (context-aware) is evaluated, we investigate ﬁfteen kinds of typical methods, which are AC [53], attention by information maximization (AIM) [54], context-aware (CA) [52], context-based (CB) [55], discriminative regional feature integration…”
Salient Object Detection: A Benchmark (2015, background): “[14] ICVS/2008 M w × h All 7 MSSS: Achanta and Sus̈strunk [55] ICIP/2009 M w × h All 8 FTS: Achanta et al.”
Saliency Detection via Dense and Sparse Reconstruction (2013, neutral): “We evaluate the proposed algorithm with seventeen state-of-the-art algorithms including IT98 [13], MZ03 [24], LC06 [25], GB06 [10], SR07 [11], AC08 [1], FT09 [2], CA10 [9], RA10 [18], RC11 [7], CB11 [14], SVO11 [6], DW11 [8], SF12 [17], LR12 [19], GS12 [21] and XL13 [22] on three benchmark data sets”
Fusing generic objectness and visual saliency for salient object detection (2011, background): “Visual attention relates to how humans process different information in a scene, and is primarily analyzed through investigating the allocation of eye fixations.”
A Benchmark of Computational Models of Saliency to Predict Human Fixations (2012, background): “Achanta and colleagues [2] and [1] present a method for salient region detection that exploits features of color and luminance and outputs full resolution saliency maps with well-defined boundaries of salient objects.”
Hierarchical Image Saliency Detection on Extended CSSD (2016, methodology): “Our method improves detection quality on many images that cannot be handled well traditionally.”
Saliency Detection via Absorbing Markov Chain (2013, neutral): “We compare our method with fifteen state-of-the-art saliency detection algorithms: the IT [16], MZ [20], LC [37], GB [14], SR [15], AC [1], FT [2], SER [31], CA [27], RC [8], CB [17], SVO [7], SF [25], LR [29] and GS [33] methods.”
Hierarchical Saliency Detection (2013, methodology): “(a) Input (b) AC [1] (c) RC [4] (d) Ours (e) Ground truth”