Saliency detection based on directional patches extraction and principal local color contrast
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Itti, 1998, A model of saliency based visual attention for rapid scene analysis, IEEE Trans. Pattern Anal. Mach. Intell., 20, 1254, 10.1109/34.730558
Koch, 1985, Shifts in selective visual attention: towards the underlying neural circuitry, Human Neurobiol., 4, 219
Olshausen, 1993, A Neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information, J. Neuroscience, 13, 4700, 10.1523/JNEUROSCI.13-11-04700.1993
Jian, 2015, Visual-patch-attention-aware saliency detection, IEEE Trans. Cybernet., 45, 1575, 10.1109/TCYB.2014.2356200
Zhang, 2018, Object-level saliency: fusing objectness estimation and saliency detection into a uniform framework, J. Visual Commun. Image Represent., 53, 102, 10.1016/j.jvcir.2018.03.002
Yan, 2017, Salient object detection via boosting object-level distinctiveness and saliency refinement, J. Visual Commun. Image Represent., 48, 224, 10.1016/j.jvcir.2017.06.013
Wang, 2018, Locality constraint distance metric learning for traffic congestion detection, Pattern Recog., 75, 272, 10.1016/j.patcog.2017.03.030
Q. Wang, J. Wan, Y. Yuan, Deep metric learning for crowdedness regression, IEEE Trans. Circuits System and Video Technology, https://10.1109/TCSVT.2017.2703920.
Jian, 2014, Facial-feature detection and localization based on a hierarchical scheme, Inf. Sci., 262, 1, 10.1016/j.ins.2013.12.001
Jian, 2014, Face-image retrieval based on singular values and potential-field representation, Signal Process., 100, 9, 10.1016/j.sigpro.2014.01.004
Toet, 2011, Computational versus psychophysical bottom-up image saliency: a comparative evaluation study, IEEE Trans. Pattern Anal. Mach. Intell., 33, 2131, 10.1109/TPAMI.2011.53
Borji, 2013, Quantitative analysis of human-model agreement in visual saliency modeling: a comparative study, IEEE Trans. Image Process., 22, 55, 10.1109/TIP.2012.2210727
Y.F. Ma, H.J. Zhang, Contrast-based image attention analysis by using fuzzy growing, in: ACM International Conference on Multimedia, November 2003, 2003, pp. 374–381.
Y. Zhai, M. Shah. Visual attention detection in video sequences using spatiotemporal cues, in: ACM International Conference on Multimedia, 2006, pp. 815–824.
Harel, 2006, Graph-based visual saliency, Adv. Neural Inf. Process. Syst., 545
X. Hou, L. Zhang, Saliency detection: a spectral residual approach, in: IEEE Conference on Computer Vision and Pattern Recognition, 2007, pp. 1–8.
T. Liu, J. Sun, N. Zheng, X. Tang, H. Shum, Learning to detect a salient object, in: IEEE Conference on Computer Vision and Pattern Recognition, 2007, pp. 1–8.
D. Gao, V. Mahadevan, N. Vasconcelos, The discriminant center-surround hypothesis for bottom-up saliency, in: Advances in Neural Information Processing Systems, 2007, pp. 497–504.
R. Achanta, S. Hemami, F. Estrada, S. Susstrunk. Frequency-tuned salient region detection, in: IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 1597–1604.
E. Rahtu, J. Kannala, M. Salo, J. Heikkilä, Segmenting salient objects from images and videos, in: European Conference on Computer Vision, 2010, pp. 366–379.
N. Murray, M. Vanrell, X. Otazu, et al., Saliency estimation using a non-parametric low-level vision model, in: IEEE Conference on Computer Vision and Pattern Recognition, 2011, pp. 433–440.
Goferman, 2012, Context-aware saliency detection, IEEE Trans. Pattern Anal. Mach. Intell., 34, 1915, 10.1109/TPAMI.2011.272
Wang, 2013, Visual saliency by selective contrast, IEEE Trans. Circuits Syst. Video Technol., 23, 1150, 10.1109/TCSVT.2012.2226528
Wang, 2013, Saliency detection by multiple-instance learning, IEEE Trans. Cybern., 43, 660, 10.1109/TSMCB.2012.2214210
Erdem, 2013, Visual saliency estimation by nonlinearly integrating features using region covariances, J. Vis., 13, 10.1167/13.4.11
Yang, 2013, Graph-regularized saliency detection with convex-hull-based center prior, IEEE Signal Proces. Lett., 20, 637, 10.1109/LSP.2013.2260737
Tong, 2014, Saliency detection with multi-scale superpixels, IEEE Signal Process. Lett., 21, 1035, 10.1109/LSP.2014.2323407
W. Zhu, S. Liang, Y. Wei, J. Sun, Saliency optimization from robust background detection, in: IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2014, pp. 2814–2821.
Cheng, 2015, Global contrast based salient region detection, IEEE Trans. Pattern Anal. Mach. Intell., 37, 569, 10.1109/TPAMI.2014.2345401
Y. Qin, H. Lu, Y. Xu, et al., Saliency detection via cellular automata, in: IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 110–119.
Chen, 2016, Visual saliency detection based on homology similarity and an experimental evaluation, J. Visual Commun. Image Represent., 40, 251, 10.1016/j.jvcir.2016.06.013
Y. Kong, L. Wang, X. Liu, H. Lu, X. Ruan, Pattern mining saliency, in: European Conference on Computer Vision, vol. 9910, 2016, pp. 583–598.
Hati, 2017, An image texture insensitive method for saliency detection, J. Visual Commun. Image Represent., 43, 212, 10.1016/j.jvcir.2017.01.007
Jian, 2017, Saliency detection using quaternionic distance based weber local descriptor and level priors, Multimedia Tools Appl., 1
Li, 2018, Saliency ranker: a new salient object detection method, J. Visual Commun. Image Represent., 50, 16, 10.1016/j.jvcir.2017.11.004
M. Jian, Q. Qi, J. Dong, Y. Yin, W. Zhang, K.M. Lam, The OUC-vision large-scale underwater image database, in: 2017 IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, 2017, pp. 1297–1302.
Jian, 2018, Integrating QDWD with pattern distinctness and local contrast for underwater saliency detection, J. Visual Commun. Image Represent., 53, 31, 10.1016/j.jvcir.2018.03.008
A. Oliva, A. Torralba, M.S. Castelhano, et al., Top-down control of visual attention in object detection, in: IEEE ICIP, vol. 1, 2003.
Wu, 2008, A top-down region dividing approach for image segmentation, Pattern Recog., 41, 1948, 10.1016/j.patcog.2007.11.020
Cholakkal, 2015, Top-down saliency with locality-constrained contextual sparse coding, BMVC
He, 2015, Exemplar-driven top-down saliency detection via deep association, Int. J. Comput. Vision, 115, 330, 10.1007/s11263-015-0822-0
Yang, 2017, Top-down visual saliency via joint crf and dictionary learning, IEEE Trans. Pattern Anal. Mach. Intell., 39, 576, 10.1109/TPAMI.2016.2547384
Li, 2002, Using the discrete wavelet frame transform to merge landsat tm and spot panchromatic images, Inf. Fusion, 3, 17, 10.1016/S1566-2535(01)00037-9
Jian, 2011, Image retrieval using wavelet-based salient regions, Imaging Sci. J., 59, 219, 10.1179/136821910X12867873897355
Mallat, 1989, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Trans. Pattern Anal. Mach. Intell., 11, 674, 10.1109/34.192463
Daubechies, 1990, The wavelet transform, time-frequency localization and signal analysis, IEEE Trans. Inf. Theory, 36, 961, 10.1109/18.57199
Unser, 1995, Texture classification and segmentation using wavelet frames, IEEE Trans. Image Process., 4, 1549, 10.1109/83.469936
Villena-González, 2016, Orienting attention to visual or verbal/auditory imagery differentially impairs the processing of visual stimuli, NeuroImage, 132, 71, 10.1016/j.neuroimage.2016.02.013
Daneshvarfard, 2018, A survey on stimuli for visual cortical function assessment in infants, Brain Dev., 40, 2, 10.1016/j.braindev.2017.07.010
Xi, 2017, Salient object detection with spatiotemporal background priors for video, IEEE Trans. Image Process., 26, 3425, 10.1109/TIP.2016.2631900
L. Nie, L. Zhang, Y. Yang, M. Wang, R. Hong, T.S. Chua, Beyond Doctors: Future Health Prediction from Multimedia and Multimodal Observations, in: ACM International Conference on Multimedia, 2015, pp. 591–600.
Nie, 2015, Disease inference from health-related questions via sparse deep learning, IEEE Trans. Knowledge Data Eng., 27, 2107, 10.1109/TKDE.2015.2399298
Nie, 2014, Bridging the vocabulary gap between health seekers and healthcare knowledge, IEEE Trans. Knowledge Data Eng., 27, 396, 10.1109/TKDE.2014.2330813
Nie, 2014, Learning to recommend descriptive tags for questions in social forums, ACM Trans. Inf. Syst., 32, 1, 10.1145/2559157
Jian, 2018, Content-based image retrieval via a hierarchical-local-feature extraction scheme, Multimedia Tools Appl., 10.1007/s11042-018-6122-2
Jing, 2018, A framework of joint low-rank and sparse regression for image memorability prediction, IEEE Trans. Circuits Systems Video Technol.
Jing, 2018, Low-rank multi-view embedding learning for micro-video popularity prediction, IEEE Trans. Knowledge Data Eng., 30, 1519, 10.1109/TKDE.2017.2785784
Jing, 2017, Predicting image memorability through adaptive transfer learning from external sources, IEEE Trans. Multimedia, 19, 1050, 10.1109/TMM.2016.2644866
Zhang, 2018, Low-rank regularized heterogeneous tensor decomposition for subspace clustering, IEEE Signal Process. Lett., 25, 333, 10.1109/LSP.2017.2748604
Liu, 2018, Structured low-rank inverse-covariance estimation for visual sentiment distribution prediction, Signal Process., 152, 206, 10.1016/j.sigpro.2018.06.001
J. Chen, X. Song, L. Nie, X. Wang, H. Zhang, T.S. Chua, Micro tells macro: predicting the popularity of micro-videos via a transductive model, in: ACM International Conference on Multimedia, 2016, pp. 898–907.
Liu, 2016, From action to activity: sensor-based activity recognition, Neurocomputing, 181, 108, 10.1016/j.neucom.2015.08.096
Y. Liu, L. Nie, L. Han, L. Zhang, D.S. Rosenblum, Action2Activity: recognizing complex activities from sensor data, in: International Conference on Artificial Intelligence (IJCAI'15), 2015, pp. 1617–1623.
Zhu, 2018, Exploring auxiliary context: discrete semantic transfer hashing for scalable image retrieval, IEEE Trans. Neural Networks Learn. Syst., 10.1109/TNNLS.2018.2797248
Li, 2018, Transfer independently together: a generalized framework for domain adaptation, IEEE Trans. Cybernetics
L. Zhu, Z. Huang, X. Chang, J. Song, H. Shen, Exploring consistent preferences: discrete hashing with pair-exemplar for scalable landmark search, in: ACM International Conference on Multimedia, 2017, pp. 726-734.
Zhu, 2017, Discrete multimodal hashing with canonical views for robust mobile landmark search, IEEE Trans. Multimedia, 19, 2066, 10.1109/TMM.2017.2729025