Visual saliency detection by spatially weighted dissimilarity

By Lijuan Duan, Chunpeng Wu, Jun Miao, Laiyun Qing, Yu Fu. Computer Vision and Pattern Recognition, 2011

303 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Sensory Systems

View Paper: https://www.semanticscholar.org/paper/9820d2e0a54d4b18f5832ef5a3d551c78e54edb4

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In Computer Vision and Pattern Recognition

In Sensory Systems

Citations in the corpus

Exploiting local and global patch rarities for saliency detection (2012, methodology): “Note that the ideas of local and global context have been (separately) considered in the past [44][17] by salient object detection/segmentation approaches, but those have not yet been tested with human fixation prediction, which is the goal of most models (including ours).”
Saliency Detection on Light Field (2017, unknown): “Bottom-up methods do not require training and rely on low-level features such as color contrast [19], pixel/patch locations [20], histogram [21], etc., for saliency detection.”
Cluster-based Co-saliency Detection (2013, unknown): “2) Spatial Cue: In human visual system, the regions near the image center draw more attention than the other regions [32]–[34].”
Saliency Detection via Dense and Sparse Reconstruction (2013, methodology): “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”
Global Contrast Based Salient Region Detection (2015, methodology): “In this work, we extensively evaluated our saliency detection 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 [”
Saliency filters: Contrast based filtering for salient region detection (2012, background): “While these algorithms are more consistent in terms of global image structures, they suffer from the involved combinatorial complexity, hence they are applicable only to relatively low resolution images, or they need to operate in spaces of reduced dimensionality [10], resulting in loss of small, po”
Efficient Salient Region Detection with Soft Image Abstraction (2013, methodology): “(a) original (b) IM[39] (c) SeR[45] (d) SUN[52] (e) SEG[43] (f) AIM[8] (g) SWD[20] (h) RC[16] (i) CA[25] (j) MZ[37]”