The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Visual features of intermediate complexity and their use in classification
Abstract
The human visual system analyzes shapes and objects in a series of stages in which stimulus features of increasing complexity are extracted and analyzed. The first stages use simple local features, and the image is subsequently represented in terms of larger and more complex features. These include features of intermediate complexity and partial object views. The nature and use of these higher-order representations remains an open question in the study of visual processing by the primate cortex. Here we show that intermediate complexity (IC) features are optimal for the basic visual task of classification. Moderately complex features are more informative for classification than very simple or very complex ones, and so they emerge naturally by the simple coding principle of information maximization with respect to a class of images. Our findings suggest a specific role for IC features in visual processing and a principle for their extraction.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Here, optimization is thought to involve the development of letter combination detectors that best constrain word identity (the diagnosticity constraint), in the same way that parts of objects act as clues to object identity in certain theoretical approaches to visual object recognition (e.g., Ullman et al., 2002).”
From A Dual-Route Approach to Orthographic Processing (2011) · cites this paper as Ullman et al., 2002
In the citing paper
“Our results indeed agree with several theories of visual processing that suggest that an initial feedforward sweep driven by bottom-up inputs builds a base representation that relies on a basic dictionary of generic features (11-13, 17, 41) before more complex tasks or visual routines can take place through recurrent projections from higher areas (20, 42, 43, 45).”
From A feedforward architecture accounts for rapid categorization (2007) · cites this paper as [17]
In the citing paper
“Key to the recognition performance of the model is the large number of tuned units across its hierarchical architecture, which is a direct consequence of the learning from natural images and represent a redundant dictionary of fragment-like features (12, 17, 41) that span a range of selectivities and invariances.”
From A feedforward architecture accounts for rapid categorization (2007) · cites this paper as [17]
Citations
Cites
Learning the parts of objects by non-negative matrix factorization
Lee, D. D., & Seung, H. S. (1999) · Nature · doi:10.1038/44565 · Cited by 14,137
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598228 · Cited by 11,798
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
Basic objects in natural categories
Rosch, E., Mervis, C. B., Gray, W. D., Johnson, D. M., & Boyes-Braem, P. (1976) · Cognitive Psychology · doi:10.1016/0010-0285(76)90013-x · Cited by 5,643
Ballard, D. H., & Brown, C. M. (1982) · Cited by 5,303
Friedman, N., Geiger, D., & Goldszmidt, M. (1997) · Machine Learning · doi:10.1023/a:1007465528199 · Cited by 4,742
Detecting faces in images: a survey
Yang, M.-H., Kriegman, D. J., & Ahuja, N. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.982883 · Cited by 3,458
Hierarchical models of object recognition in cortex
Riesenhuber, M., & Poggio, T. (1999) · Nature Neuroscience · doi:10.1038/14819 · Cited by 3,309
Face recognition by elastic bunch graph matching
Wiskott, L., Fellous, J., Kuiger, N., & von der Malsburg, C. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598235 · Cited by 2,916
Flagged first, then by citations
Cited by
Sorted by citations
Items 301–400 of 599
Henderson, M. M., Tarr, M. J., & Wehbe, L. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.1822-22.2023 · Cited by 19
Evans, M. H., Fox, C. W., Pearson, M., & Prescott, T. (2010) · Simulation of Adaptive Behavior · doi:10.1007/978-3-642-15193-4_17 · Cited by 14
Gur, M. (2015) · Trends in Neurosciences · doi:10.1016/j.tins.2015.02.005 · Cited by 15
Computational object recognition: a biologically motivated approach
Kietzmann, T. C., Lange, S., & Riedmiller, M. (2008) · Biological Cybernetics · doi:10.1007/s00422-008-0281-6 · Cited by 15
Underwater image feature extraction and matching based on visual saliency detection
Zhang, L., He, B., Song, Y., & Yan, T. (2016) · OCEANS 2016 - Shanghai · doi:10.1109/oceansap.2016.7485370 · Cited by 14
Visual prototypes in the ventral stream are attuned to complexity and gaze behavior
Rose, O., Johnson, J. K., Wang, B., & Ponce, C. R. (2021) · Nature Communications · doi:10.1038/s41467-021-27027-8 · Cited by 13
Perceptual Segmentation: Combining Image Segmentation With Object Tagging
Bergman, R., & Nachlieli, H. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2088970 · Cited by 14
Idiosyncratic, Retinotopic Bias in Face Identification Modulated by Familiarity
Castello, M. V. D. O., Taylor, M. K., Patrick Cavanagh, B., & Gobbini, M. I. (2018) · eNeuro · doi:10.1523/eneuro.0054-18.2018 · Cited by 14
Identifying faces across variations in lighting
Tarr, M. J., Georghiades, A. S., & Jackson, C. D. (2008) · ACM Transactions on Applied Perception · doi:10.1145/1279920.1279924 · Cited by 17
Logical rules of visual brain: From anatomy through neurophysiology to cognition
Przybyszewski, A. W. (2008) · Cognitive Systems Research · doi:10.1016/j.cogsys.2008.08.006 · Cited by 13
Object categorization by wild ranging birds—Winter feeder experiments
Nováková, N., Veselý, P., & Fuchs, R. (2017) · Behavioural Processes · doi:10.1016/j.beproc.2017.08.002 · Cited by 13
Object Recognition with an Optimized Ventral Stream Model Using Genetic Programming
Clemente, E., Gustavo Olague, A., Dozal, L., & Mancilla, M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-29178-4_32 · Cited by 13
"Effect of Noise Blur and Motion on Global Appearance Face Recognition based Methods Performance"
Cherifi, D., Radji, N., & Naït-Ali, A. (2011) · International Journal of Computer Applications · doi:10.5120/2019-2723 · Cited by 13
BIOLOGICALLY MOTIVATED INCREMENTAL OBJECT PERCEPTION BASED ON SELECTIVE ATTENTION
Won, W.-J., YEO, J., Ban, S.-W., & Lee, M. (2007) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s021800140700596x · Cited by 13
Mirror-image relations in category learning
Rentschler, I., & Jüttner, M. (2006) · Visual Cognition · doi:10.1080/13506280600574784 · Cited by 13
Receptive Field Structures for Recognition
Balas, B., & Sinha, P. (2006) · Neural Computation · doi:10.1162/089976606775623315 · Cited by 13
Unsupervised learning of multiple objects in images
Titsias, M. K. (2005) · ERA · Cited by 13
Farahat, A., Effenberger, F., & Vinck, M. A. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.08.021 · Cited by 12
Feature Extraction and Representation for Face Recognition
Saquib, M., Hellwich, O., & Riaz, Z. (2010) · InTech eBooks · doi:10.5772/8936 · Cited by 12
Two pathways to stimulus encoding in category learning?
Davis, T. H., Love, B. C., & Maddox, W. T. (2009) · Memory & Cognition · doi:10.3758/mc.37.4.394 · Cited by 12
Vocalization categorization behavior explained by a feature-based auditory categorization model
Kar, M., Pernía, M., Williams, K., Parida, S., Schneider, N. A., McAndrew, M., Kumbam, I., & Sadagopan, S. (2022) · eLife · doi:10.7554/elife.78278 · Cited by 12
The Role of Surface-Based Representations of Shape in Visual Object Recognition
Reppa, I., Greville, W. J., & Leek, E. C. (2015) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470218.2015.1014379 · Cited by 12
Fragment-Based Learning of Visual Object Categories in Non-Human Primates
Kromrey, S., Maestri, M., Hauffen, K., Bart, E., & Hegdé, J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015444 · Cited by 12
Object recognition in clutter: cortical responses depend on the type of learning
Hegdé, J., Thompson, S. K., Brady, M., & Kersten, D. (2012) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00170 · Cited by 12
Multiple Ordinal Regression by Maximizing the Sum of Margins
Hamsici, O. C., & Martı́nez, A. M. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2477321 · Cited by 12
Explanation-based feature construction
Lim, S. H., Wang, L.-L., & DEJONG, G. F. (2007) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · Cited by 11
The interplay of holistic shape, local feature and color information in object categorization
Rokszin, A. A., Győri-Dani, D., Linnert, S., Krajcsi, A., Tompa, T., & Csifcsák, G. (2015) · Biological Psychology · doi:10.1016/j.biopsycho.2015.05.002 · Cited by 13
Cellular Evolution: What's in a Mitochondrion?
Howe, C. J. (2008) · Current Biology · doi:10.1016/j.cub.2008.04.007 · Cited by 11
Class-Based Matching of Object Parts
Bart, E., & Ullman, S. (2005) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvpr.2004.312 · Cited by 12
Self-citationAdvancing Crowd Object Detection: A Review of YOLO, CNN and ViTs Hybrid Approach*
Ali, M. A. M., Aly, T., Raslan, A. T., Gheith, M. H., & Amin, E. A. (2024) · Journal of Intelligent Learning Systems and Applications · doi:10.4236/jilsa.2024.163011 · Cited by 18
Exploration of complex visual feature spaces for object perception
Leeds, D., Pyles, J. A., & Tarr, M. J. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00106 · Cited by 11
Visual Classification by a Hierarchy of Extended Fragments
Ullman, S., & Epshtein, B. (2006) · Lecture notes in computer science · doi:10.1007/11957959_17 · Cited by 11
Self-citationExperience-driven formation of parts-based representations in a model of layered visual memory
Jitsev, J. (2009) · Frontiers in Computational Neuroscience · doi:10.3389/neuro.10.015.2009 · Cited by 11
Components for Object Detection and Identification
Heisele, B., Riskov, I., & Morgenstern, C. (2006) · Lecture notes in computer science · doi:10.1007/11957959_12 · Cited by 11
Boosting algorithms for simultaneous feature extraction and selection
Saberian, M. J., & Vasconcelos, N. M. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6247959 · Cited by 11
Class-Based Feature Matching Across Unrestricted Transformations
Bart, E., & Ullman, S. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.70818 · Cited by 11
Self-citationCreating Objects and Object Categories for Studying Perception and Perceptual Learning
Hauffen, K., Bart, E., Brady, M., Kersten, D., & Hegdé, J. (2012) · Journal of Visualized Experiments · doi:10.3791/3358 · Cited by 11
Adaptive Efficient Coding of Correlated Acoustic Properties
Lü, K., Liu, W., Dutta, K., Zan, P., Fritz, J. B., & Shamma, S. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.0141-19.2019 · Cited by 11
Lin, C.-P., Chen, Y.-P., & Hung, C. P. (2014) · Journal of Neurophysiology · doi:10.1152/jn.00485.2013 · Cited by 10
Smith, L. B., & Pereira, A. F. (2009) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195331059.003.0006 · Cited by 9
Dimensionality Reduction Based on ICA for Regression Problems
Kwak, N., & Kim, C. (2006) · Lecture notes in computer science · doi:10.1007/11840817_1 · Cited by 10
Distinctive and compact features
Akselrod-Ballin, A., & Ullman, S. (2008) · Image and Vision Computing · doi:10.1016/j.imavis.2008.03.005 · Cited by 10
Self-citationHolzinger, Y., Ullman, S., Harari, D., Behrmann, M., & Avidan, G. (2019) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01420 · Cited by 10
Self-citationSatellite Features for the Classification of Visually Similar Classes
Epshtein, B., & Ullman, S. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.262 · Cited by 10
Self-citationA biological model of object recognition with feature learning
Louie, J. (2003) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 10
Modularity in Nervous Systems—a Key to Efficient Adaptivity for Deep Reinforcement Learning
Schilling, M., Hammer, B., Ohl, F. W., Ritter, H. J., & Wiskott, L. (2023) · Cognitive Computation · doi:10.1007/s12559-022-10080-w · Cited by 10
Story retrieval and comparison using concept patterns
Krakauer, C. E. (2012) · Cited by 9
Image Description with Local Patterns: An Application to Face Recognition
Zhou, W., Ahrary, A., & Kamata, S. (2012) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.e95.d.1494 · Cited by 9
Peters, J. C., Reithler, J., & Goebel, R. (2012) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2012.00012 · Cited by 9
Tan, C., & Poggio, T. (2016) · PLoS ONE · doi:10.1371/journal.pone.0150980 · Cited by 9
Geramifard, A. (2012) · Cited by 9
A Comparison of Features in Parts-Based Object Recognition Hierarchies
Hasler, S., Wersing, H., & Körner, E. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74695-9_22 · Cited by 9
Intermediate Features and Informational-level Constraint on Analogical Retrieval
Finlayson, M. A., & Winston, P. H. (2005) · eScholarship (California Digital Library) · Cited by 9
Unsupervised learning of visual features through Spike Timing Dependent Plasticity
Masquelier, T., & Thorpe, S. J. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030031.eor · Cited by 9
Modulation of early ERPs by accurate categorization of objects in scenes
Cesarei, A. D., Peverato, I. A., Mastria, S., & Codispoti, M. (2015) · Journal of Vision · doi:10.1167/15.8.14 · Cited by 9
Modelling 3D Scanned Data to Visualise and Analyse the Built Environment for Regeneration
Arayıcı, Y., Hamilton, A., & Gamito, P. S. (2006) · Northumbria Research Link (Northumbria University) · Cited by 9
Interpretable Compositional Representations for Robust Few-Shot Generalization
Mishra, S., Zhu, P., & Saligrama, V. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3212633 · Cited by 11
Chen, W., Niu, L., Liu, S., Ma, S., Li, H., & Yang, Z. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2327197 · Cited by 8
Object Recognition via Adaptive Multi-level Feature Integration
Wang, M., Wu, Y., Li, G., & Zhou, X. (2010) · Asia-Pacific Web Conference · doi:10.1109/apweb.2010.24 · Cited by 8
Evaluating information contributions of bottom-up and top-down processes
Yang, X., Wu, T., & Zhu, S. C. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459386 · Cited by 8
Prediction of complex stimuli across saccades
Osterbrink, C., & Herwig, A. (2021) · Journal of Vision · doi:10.1167/jov.21.2.10 · Cited by 7
Effects of cognitive load and type of object on the visual looming bias
McGuire, A., Ciersdorff, A., Gillath, O., & Vitevitch, M. S. (2021) · Attention Perception & Psychophysics · doi:10.3758/s13414-021-02271-8 · Cited by 8
Measuring the speed of newborn object recognition in controlled visual worlds
Wood, J. N., & Wood, S. M. W. (2016) · Developmental Science · doi:10.1111/desc.12470 · Cited by 8
A similarity-based approach to perceptual feature validation
Cooke, T., Steinke, F., Wallraven, C., & Bülthoff, H. (2005) · Symposium on Applied Perception in Graphics and Visualization (APGV) · doi:10.1145/1080402.1080413 · Cited by 8
Inducing features from visual noise
Cohen, A. L., Shiffrin, R. M., Gold, J. M., Ross, D. A., & Ross, M. G. (2007) · Journal of Vision · doi:10.1167/7.8.15 · Cited by 8
Modeling the adaptive visual system: a survey of principled approaches
Schwabe, L., & Obermayer, K. (2003) · Neural Networks · doi:10.1016/j.neunet.2003.07.009 · Cited by 8
Learning from weak representations using distance functions and generative models
Bar-Hillel, A. (2006) · Cited by 8
Asking the Right Questions About the Psychology of Human Inquiry: Nine Open ChallengesPreprint
Coenen, A., Nelson, J. D., & Gureckis, T. M. (2017) · doi:10.31234/osf.io/h457v · Cited by 9
Ghodrati, M., Rajaei, K., & Ebrahimpour, R. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00078 · Cited by 7
Layher, G., Schrodt, F., Butz, M. V., & Heiko Neumann, B. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.01287 · Cited by 9
Ihou, K. E., Bouguila, N., & Bouachir, W. (2020) · Pattern Analysis and Applications · doi:10.1007/s10044-020-00917-1 · Cited by 7
Transcranial Magnetic Stimulation Studies of Face Processing
Pitcher, D., Walsh, V., & Duchaine, B. (2011) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199559053.013.0019 · Cited by 9
Patch-Based Experiments with Object Classification in Video Surveillance
Wijnhoven, R. G. J., & de With, P. H. N. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74607-2_26 · Cited by 7
A Bayesian inference theory of attention: neuroscience and algorithms
Chikkerur, S. S., Serre, T., & Poggio, T. (2009) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 7
Parkington, K. B., & Itier, R. J. (2019) · Brain Research · doi:10.1016/j.brainres.2019.146343 · Cited by 7
Fragmented ambiguous objects: Stimuli with stable low-level features for object recognition tasks
Olman, C. A., Espensen-Sturges, T. D., Muscanto, I., Longenecker, J., Burton, P. C., Grant, A., & Sponheim, S. R. (2019) · PLoS ONE · doi:10.1371/journal.pone.0215306 · Cited by 7
Sparse visual models for biologically inspired sensorimotor control
Li, Y., & Jabri, M. A. (2003) · CogPrints (University of Southampton) · Cited by 7
Face recognition based on ordinal correlation
Tjahyadi, R., Liu, W., An, S., & Venkatesh, S. (2007) · International Journal of Intelligent Systems Technologies and Applications · doi:10.1504/ijista.2007.014261 · Cited by 7
Hierarchical Models of the Visual System
Ricci, M. R., & Serre, T. (2020) · Springer eBooks · doi:10.1007/978-1-4614-7320-6_345-2 · Cited by 8
Combining Variable Selection with Dimensionality Reduction
Wolf, L., & Bileschi, S. (2005) · doi:10.21236/ada454990 · Cited by 7
Hierarchical Models of the Visual System
Ricci, M. R., & Serre, T. (2022) · Springer eBooks · doi:10.1007/978-1-0716-1006-0_345 · Cited by 9
Modeling Efficient Classification as a Process of Confidence Assessment and Delegation
Autio, I. (2008) · Työväentutkimus Vuosikirja · Cited by 7
Detecting people in Cubist art
Ginosar, S., Haas, D., Brown, T., & Malik, J. (2015) · AI Matters · doi:10.1145/2735392.2735398 · Cited by 12
Nonnegative Matrix Factorizations Performing Object Detection and Localization
Casalino, G., Buono, N. D., & Minervini, M. (2012) · Applied Computational Intelligence and Soft Computing · doi:10.1155/2012/781987 · Cited by 6
A surface-based code contributes to visual shape perception
Arguin, M., Marleau, I., Aubin, M., Zahabi, S., & Leek, E. C. (2019) · Journal of Vision · doi:10.1167/19.11.6 · Cited by 6
Mini review: Individual differences and domain-general mechanisms in object recognition
Gauthier, I., Cha, O., & Chang, T. (2022) · Frontiers in Cognition · doi:10.3389/fcogn.2022.1040994 · Cited by 3
A dual-stream hybrid model for blind image quality assessment
Tong, B., Kong, F., Kang, T., Luo, T., & Shi, Z. (2023) · Digital Signal Processing · doi:10.1016/j.dsp.2023.104109 · Cited by 7
Layher, G., Liebau, H., Niese, R., Al-Hamadi, A. K., Michaelis, B., & Heiko Neumann, B. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24085-0_24 · Cited by 6
Generative Models for Labeling Multi-object Configurations in Images
Amit, Y., & Trouvé, A. (2006) · Lecture notes in computer science · doi:10.1007/11957959_19 · Cited by 6
Reppa, I., & Leek, E. C. (2019) · Attention Perception & Psychophysics · doi:10.3758/s13414-019-01698-4 · Cited by 6
Whatever Happened to Object-Centered Representations?
Hayward, W. G. (2012) · Perception · doi:10.1068/p7338 · Cited by 6
Rousselet, G. A., Macé, M. J. ‐., & Fabre‐Thorpe, M. (2004) · Neurocomputing · doi:10.1016/j.neucom.2004.01.127 · Cited by 6
Detecting and Localizing 3D Object Classes using Viewpoint Invariant Reference Frames
Toews, M., & Arbel, T. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408832 · Cited by 6
Main Peak Finding for Signboard Recognition under Adverse Conditions
Zin, T. T., Koh, S.-S., & Hama, H. (2006) · Cited by 6
Generalization in Holistic versus Analytic Processing of Faces
Bicego, M., Salah, A. A., Grosso, E., Massimo Tistarelli, A., & Akarun, L. (2007) · International Conference on Image Analysis and Processing · doi:10.1109/iciap.2007.4362785 · Cited by 6
Using Spatio-Temporal Patches for Simultaneous Estimation of Edge Strength, Orientation, and Motion
Stein, A. N., & Hebert, M. (2006) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2006.209 · Cited by 6
Detecting People in Cubist ArtPreprint
Ginosar, S., Haas, D., Brown, T., & Malik, J. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.6235 · Cited by 6
Li, D. S. P., & Bonner, M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.24.461733 · Cited by 6
A method for real-time visual stimulus selection in the study of cortical object perception
Leeds, D., & Tarr, M. J. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.071 · Cited by 5
Cumulative inhibition in neural networks
Tjøstheim, T. A., & Balkenius, C. (2018) · Cognitive Processing · doi:10.1007/s10339-018-0888-z · Cited by 5
Metadata
- Authors
- Ullman, S., Vidal-Naquet, M., & Sali, E.
- Affiliations
- Weizmann Institute of Science (IL)
- Corresponding author
- Ullman, Shimon
- ORCID
- Ullman, Shimon
- Venue
- Nature Neuroscience
- Published
- 2002-06-10
- Volume
- 5
- Issue
- 7
- Pages
- 682-687
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W2037090920
- Cited by
- 600
- References
- 30
- Citations by year
- 2025: 6 · 2024: 10 · 2023: 10 · 2022: 13 · 2021: 17 · 2020: 9 · 2019: 15 · 2018: 10 · 2017: 17 · 2016: 20 · 2015: 33 · 2014: 30 · 2013: 24 · 2012: 39
- FWCI
- 14.07
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- visual feature extraction, object classification, information maximization, feature complexity, visual cortex, hierarchical visual processing, primate visual system
- MeSH terms
- Computer Simulation, Face, Humans, Image Processing, Computer-Assisted, Pattern Recognition, Visual
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
