Visual features of intermediate complexity and their use in classification

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

Eigenfaces for Recognition

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

Computer vision

Ballard, D. H., & Brown, C. M. (1982) · Cited by 5,303

Bayesian network classifiers

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

Applying Information Theory to Validate Commanders' Critical Information Requirements

Timms, M. A. C., Mandel, D. R., & Nelson, J. D. (2024) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003396307-18 · Cited by 1

Natural Scene and Object Perception Based on Statistical Image Features: Psychophysics and EEG

Orima, T., Kurosawa, F., Sekimoto, T., & Motoyoshi, I. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.0859-25.2025 · Cited by 2

The Visual Hierarchy Mirage: Seeing Trees in a Graph

Zucker, S. W. (2013) · Advances in computer vision and pattern recognition · doi:10.1007/978-1-4471-5195-1_11 · Cited by 1

Face tracking in low resolution videos under illumination variations

Zou, W. W. W., Chellappa, R., & Yuen, P. C. (2011) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2011.6116672 · Cited by 1

Create visual word pairs dynamically based on sparse codes of SIFT features for image categorization

Wu, L., Huang, Y., Sun, W., & Ke, J. (2012) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2012.6234525 · Cited by 1

The effective resolution of correlation filters applied to natural scenes

Vidal-Naquet, M. J., & Tanifuji, M. (2007) · 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.2007.383368 · Cited by 1

Visual dictionaries in the Brain: Comparing HMAX and BOW

Ramakrishnan, K., Groen, I. I. A., Scholte, H. S., Smeulders, A. W. M., & Ghebreab, S. (2014) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2014.6890312 · Cited by 1

Latent Model Clustering and Applications to Visual Recognition

Polak, S., & Shashua, A. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4409051 · Cited by 1

Multimap formation in visual cortex

Jain, R., Millin, R., & Mel, B. W. (2015) · Journal of Vision · doi:10.1167/15.16.3 · Cited by 1

Integration and goal-guided scheduling of bottom-up and top-down computing processes in hierarchical models

Zhu, S. C., & Wu, T. (2011) · Cited by 1

Random Variable Transformations

Ogunnaike, B. A. (2009) · CRC Press eBooks · doi:10.1201/b17197-10 · Cited by 1

Elements for a Neural Theory of the Processing of Dynamic Faces

Serre, T., & Giese, M. A. (2010) · The MIT Press eBooks · doi:10.7551/mitpress/9780262014533.003.0014 · Cited by 1

General Principles of the Objects Recognition

Fuchs, R., Veselý, P., & Nácarová, J. (2019) · Springer briefs in animal sciences · doi:10.1007/978-3-030-12404-5_4 · Cited by 1

Neural Sensitivity to Mutual Information in Intermediate-Complexity Face Features Changes during Childhood

Balas, B., Harel, A., Auen, A., & Saville, A. L. (2019) · Brain Sciences · doi:10.3390/brainsci9070154 · Cited by 1

Multimodal Visual Features on Rhizome Image Classification Using Support Vector Machines

Hamdani, S. K., Fahmi, H., Khudzaifah, M., Kusumastuti, A., Jauhari, M. N., & Sari, W. P. (2024) · doi:10.1109/icoict61617.2024.10698345 · Cited by 1

Enhancing object recognition: The role of object knowledge decomposition and component-labeled datasets

Xiong, N., Wang, N., Li, H., Zhu, G., Zhang, L., Shah, S. A. A., & Bennamoun, M. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128969 · Cited by 1

Diagnostic parts are not exclusive in the search template for real-world object categories

Wurth, M., & Reeder, R. (2019) · Acta Psychologica · doi:10.1016/j.actpsy.2019.03.006 · Cited by 1

Maximal Dependence Capturing as a Principle of Sensory Processing

Raj, R., Dahlen, D., Duyck, K., & Yu, C. R. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.857653 · Cited by 1

An Approach to Visual Scenes Matching with Curvilinear Regions

Pérez-Lorenzo, J. M., Bandera, A. J., López, P. R., Marfil, R., & Vázquez-Martín, R. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-73055-2_43 · Cited by 1

Towards a unified account of face (and maybe object) processing

Tan, C. (2012) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 1

High-level visual object representations in inferior temporal cortex

Mur, M. (2011) · Research Publications (Maastricht University) · doi:10.26481/dis.20111202mm · Cited by 1

A Novel Object Categorization Model with Implicit Local Spatial Relationship

Wu, L., Luo, S., & Sun, W. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13318-3_18 · Cited by 1

Learning Nonlinear Visual Processing from Natural Images

Lindgren, J. T. (2008) · Työväentutkimus Vuosikirja · Cited by 1

Classifying a New Descriptor Based on Marr’s Visual Theory

Pérez-Lorenzo, J. M., Galán, S. G., Bandera, A. J., Vázquez-Martín, R., & Marfil, R. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02264-7_22 · Cited by 1

Visual codebook construction for class-specific recognition

Gao, J., Sang, N., Gao, C., Tang, Q., & Jun, S. (2009) · Optical Engineering · doi:10.1117/1.3160333 · Cited by 1

Some relationships between minimum Bayes error and information theoretical feature extraction

Vasconcelos, M., & Vasconcelos, N. M. (2005) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.604289 · Cited by 1

Object Detection Using Multi-local Feature Manifolds

Danielsson, O., Carlsson, S., & Sullivan, J. (2008) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2008.22 · Cited by 1

Perceptual Similarities Amongst Novel, 3D Objects

Cooke, T. (2007) · MPG.PuRe (Max Planck Society) · Cited by 1

Multiclass object recognition inspired by the ventral visual pathway

Mutch, J. V. (2010) · cIRcle (University of British Columbia) · doi:10.14288/1.0051724 · Cited by 1

A Model of Analogical Retrieval Using Intermediate Features

Finlayson, M. A., & Winston, P. H. (2004) · eScholarship (California Digital Library) · Cited by 1

Modeling Visual Minutiae: Gestures, Styles, and Temporal Patterns

Ginosar, S. (2020) · eScholarship (California Digital Library) · Cited by 1

Body shape as a visual feature: evidence from spatially-global attentional modulation in human visual cortexPreprint

Thorat, S., & Peelen, M. V. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.17.469004 · Cited by 1

Applying Information Theory to Validate Commanders’ Critical Information RequirementsPreprint

Timms, M. A. C., Mandel, D. R., & Nelson, J. D. (2023) · doi:10.31234/osf.io/n8xka · Cited by 1

Detecção de cenas em segmentos semanticamente complexos

Lopes, B. L. (2014) · doi:10.11606/d.55.2014.tde-12082014-101301 · Cited by 1

Computational modeling of face recognition

Wallraven, C., Schwaninger, A., & Bülthoff, H. (2003) · MPG.PuRe (Max Planck Society) · Cited by 1

Etude sur la pensée animale: continuité neuro-cognitive de la catégorisation visuelle

Cauchoix, M. (2012) · Thèses en ligne de l'Université Toulouse III (Université Toulouse III)

Dissecting Neural Circuits for Vision in Nonhuman Primates using fMRI-Guided Electrophysiology and Optogenetics

Ohayon, S. (2014) · CaltechTHESIS (California Institute of Technology) · doi:10.7907/wd1d-w918

Open N-Grams and Discriminant Features in Text World: An Empirical Study

Lapedriza, À. (2004)

Project PRAKASH: Development of Object Perception Following Long-term Visual Deprivation.

Sinha, P. (2005)

Cross-domain self organizing maps

Fidelholtz, E. L. (2006)

Applications of Kernel Methods

Camps‐Valls, G., Martínez‐Ramón, M., & Rojo‐Álvarez, J. L. (2009) · IGI Global eBooks · doi:10.4018/978-1-60566-010-3.ch009

What are Intermediate‐Level Visual Features?

Zucker, S. W. (2013) · Wiley eBooks · doi:10.1002/9781118329016.ch18

Functional Constraints on Network Topology via Generalized Sparse Representations

Rao, A. R., & Cecchi, G. (2011) · Springer eBooks · doi:10.1007/978-1-4614-0724-9_5

What happens next and when "next" happens: Mechanisms of spatial and temporal predictionPreprint

Wyatte, D. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1407.5328

Measuring External Face Appearance for Face Classification

Masip, D., Lapedriza, À., & Vitria, J. M. (2007) · doi:10.5772/4842

Comparing feature matching for visual object categorization: MAX vs. bag-of-words

Wijnhoven, R. G. J., With, D. P. H. N., Tjalkens, T. J., & Willems, F. M. J. (2009) · TU/e Research Portal

Extending Recognition in a Changing Environment

Harari, D., & Ullman, S. (2013) · International Conference on Computer Vision Theory and Applications · doi:10.5220/0004281106320640

A study in human attention to guide computational action recognition

Sinai, S. (2014) · DSpace@MIT (Massachusetts Institute of Technology)

A computational model of visual interpretation

Biswas, D., & Kanti, K. (2006)

Segmentação automática de vídeo em cenas baseada em coerência entre tomadas

Trojahn, T. H. (2014) · doi:10.11606/d.55.2014.tde-20052014-152446

Recognizing complex faces and gaits via novel probabilistic models

Venkatasubramanian, I. V. K. (2010)

A hierarchical framework for object recognitionPreprint

Moazzezi, R. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.7762

Robust object recognition based on HMAX model architecture

Chang, Y., Xu, Z., Zhang, J., Fu, C., & Gao, C. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.999350

On the resolution of local ambiguity in visual recognition: Modeling by tree-structured Markov random fields

Date, A. (2005) · Systems and Computers in Japan · doi:10.1002/scj.20367

Report Fragment-Based Learning of Visual Object Categories

Hegdé, J., Bart, E., & Kersten, D. (2008)

An optimization approach to achieve unsupervised segmentation and binding in a dynamical network

Rao, A. R., Cecchi, G., Peck, C., & Kozloski, J. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2006.1716673

Biologically Inspired Class-Specific Codebook Construction

Gao, J., Gao, C., Sang, N., & Tang, Q. (2009) · International Workshop on Education Technology and Computer Science · doi:10.1109/etcs.2009.552

Workshop on Generic Object Recognition and Categorization

Dickinson, S., Leonardis, A., & Schiele, B. (2004) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvpr.2004.479

Representational Shifts during Category Acquisition: A Preference for Features that Provide Information for Multiple Categories

Fink, M., & Ben‐Shakhar, G. (2005)

Transferring Characteristic Proportions to Modify the Artistic Style of Cartoons

Buchanan, P., & Doggett, M. (2012) · Lund University Publications (Lund University)

Rough Set Theory of Shape Perception

Przybyszewski, A. W. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74205-0_78

Class-specific codebook construction for biologically inspired recognition

Gao, J., Gao, C., Sang, N., & Tang, Q. (2009) · IEEE International Advance Computing Conference · doi:10.1109/iadcc.2009.4809073

OBSERVATIONS OF HIGH-DIMENSIONAL DATA

Yörük, E. (2011)

Machine Recognition of Objects

Poggio, T., & Ullman, S. (2021) · Springer eBooks · doi:10.1007/978-3-030-63416-2_793

Local vs. Global

Riccio, D., Casanova, A., & Fenu, G. (2014) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-4666-5966-7.ch009

Low-latency Image Recognition with GPU-accelerated Convolutional Networks for Web-based Services

Huang, F. J. (2014)

Keeping visual-auditory associations in mind: The impact of detail and meaningfulness on crossmodal working memory load

Gilman, A. (2009) · University of New Hampshire Scholars Repository (University of New Hampshire at Manchester)

EMOTIONAL ENGAGEMENT AND BRAIN POTENTIALS

Dottorato, C., Codispoti, M., & Mattarozzi, K. (2014)

The Featural Code of Face Perception: Three Studies of Human and Automatic Visual Face Processing

Nestor, A. (2009) · ProQuest Demo Repository · doi:10.7301/z00g3hdm

THEORETICAL NOTES Finding Useful Questions: On Bayesian Diagnosticity, Probability, Impact, and Information Gain

Nelson, J. D. (2005)

EMPIRICAL EVIDENCE AND THEORETICAL ANALYSIS OF FEATURE CREATION DURING CATEGORY ACQUISITION

FINK, M., Ben‐Shakhar, G., & Horn, D. (2004) · Neural Computation and Psychology Workshop (NCPW) · doi:10.1142/9789812702784_0013

Detection of free-form objects using appearance-based local features

Vicente, M. A., Fernández, C., Payá, L., & Gil, A. (2007)

Learning transformation invariance for pairwise image matching

Chen, X. (2008) · doi:10.32657/10356/41433

Generic Object Recognizer Design

Mundy, J. L. (2006) · Computer Vision for Interactive and Intelligent Environment (CVIIE) · doi:10.1109/cviie.2005.9

Intermediate Features Improve Incremental Analogical Mapping

Finlayson, M. A., & Winston, P. H. (2005) · eScholarship (California Digital Library)

Disentangling the representations of object shape and object category in the brain: the animate-inanimate distinction

Proklova, D. (2017) · doi:10.15168/11572_367590

Rumelhart Symposium: Integrating Human and Machine Vision: In Honor of Shimon Ullman

Tarr, M. J., Behrmann, M., Christof Koch, A., Logothetis, N. K., & Malach, R. (2008) · eScholarship (California Digital Library)

Constructing the Search Template: Episodic and Semantic Influences on Categorical Template Formation

Pinto, G., & Juan, D. G. (2016) · Civil War Book Review

Graphical Models of the Visual Cortex

Dean, T. (2010)

Invariant Model Combining Geometry and Appearance for Facial Detection and Gender Classification From Arbitrary Viewpoints

Taffar, M., & Miguet, S. (2017) · Advances in multimedia and interactive technologies book series · doi:10.4018/978-1-5225-2848-7.ch009

Using Motion and Internal Supervision in Object RecognitionPreprint

Harari, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.05455

Early Vision and Cognitive Penetrability

Raftopoulos, A. (2019) · Springer eBooks · doi:10.1007/978-3-030-10445-0_3

Charlie Gross: An inspiration

Perrett, D. I. (2020) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2020.101928

Invariant Object Representation Based on Principle of Maximal Dependence CapturingPreprint

Raj, R., Dahlen, D., Duyck, K., & Yu, C. R. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/662130

Repetition blindness for words and pictures: A failure to form stable type representations?

Harris, I. M., Hayward, W. G., Seet, M. S., & Andrews, S. (2021) · Memory & Cognition · doi:10.3758/s13421-021-01146-5

Invariant visual object recognition and shape processing in rats

Zoccolan, A. (2015)

Learning graphical models with limited observations of high-dimensional data

Yörük, E. (2011)

Object Recognition in Psychosis: Altered Connectivity Between Levels of the Visual Perceptual Hierarchy

Bansal, S. (2021) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2021.08.005

Feature Extraction

Ardeshir Goshtasby, A. (2012) · Advances in computer vision and pattern recognition · doi:10.1007/978-1-4471-2458-0_4

Categorization

Mack, M. L., Richler, J. J., Palmeri, T. J., & Gauthier, L. (2009) · Wiley eBooks · doi:10.1002/9780470478509.neubb001021

Event Recognition—Biological

Thompson, W., Fleming, R. W., Creem-Regehr, S., & Stefanucci, J. K. (2011) · A K Peters/CRC Press eBooks · doi:10.1201/b10927-24

Discussion: Biological Plausibility of the Model

Jang, J., & Paik, S. (2022) · KAIST research series · doi:10.1007/978-981-19-0031-0_5

Invariant Model Combining Geometry and Appearance for Facial Detection and Gender Classification From Arbitrary Viewpoints

Taffar, M., & Miguet, S. (2018) · IGI Global eBooks · doi:10.4018/978-1-5225-6912-1.ch034

Constructing the Search Template: Episodic and Semantic Influences on Categorical Template Formation

Pinto, J. G. (2016) · doi:10.31390/gradschool_theses.4405

Machine Recognition of Objects

Poggio, T., & Ullman, S. (2014) · Springer eBooks · doi:10.1007/978-0-387-31439-6_793

Observing versus creating flowers: a review of relevance for art therapy

Huss, E., Nagamine, M., & Zaccai, M. (2025) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2025.1504057

Given the birds, where is the flock? Visual estimation of the location of collections of pointsPreprint

Ota, K., Wu, Q., Mamassian, P., & Maloney, L. T. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.10.664170

Traitrements cognitifs mis en jeu dans la perception visuelle de scènes complexes et conséquences sur l'indexation automatique d'images

Li, J. (2008) · HAL

Arts visuels et cognition : Développement visuel et formation du jugement esthétique

Mottier, H. (2020) · theses.fr (ABES) · doi:10.70675/53f1226bz8d14z4b23zb5f0zb739e535728c

The psychological reality and neural basis of intermediate complexity features in perceptual categorization

Harel, A., Ullman, S., Epshtein, B., & Shlomo Bentin, A. (2010) · Journal of Vision · doi:10.1167/6.6.622

Mutual information of image fragments predicts categorization in humans: Electrophysiological and behavioral evidence

Harel, A., Ullman, S., Epshtein, B., & Bentin, S. (2007) · Vision Research · doi:10.1016/j.visres.2007.04.004 · Cited by 40

A new perspective to cognitive neuroscience: understanding the information processing strategies of human brain

Duan, Y. (2024) · Enlighten: Theses (The University of Glasgow) · doi:10.5525/gla.thesis.84568

Using graphical models to infer multiple visual classification features

Ross, M. G., & Cohen, A. L. (2009) · Journal of Vision · doi:10.1167/9.3.23 · Cited by 10

Cortical representations supporting coarse and fine object categorization

Henderson, M. M., Park, S., Wehbe, L., & Tarr, M. J. (2025) · Journal of Vision · doi:10.1167/jov.25.9.2874