On the Role of Object-Specific Features for Real World Object Recognition in Biological Vision
By Thomas Serre, Maximilian Riesenhuber, Jennifer Louie, Tomaso A. Poggio. 2002
79 citations recorded by the source corpus.
Subfields: Biophysics, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biophysics
- Tomaso A. Poggio
- Jake V. Bouvrie
- Thomas Serre
- Lior Wolf
- Gabriel Kreiman
- Charles F. Cadieu
- Nikolaus Kriegeskorte
- Jaume Amores
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Andrew Zisserman
- Xiaogang Wang
- Trevor Darrell
- Dacheng Tao
- Andrea Vedaldi
- Ming-Hsuan Yang
- Jitendra Malik
Citations in the corpus
- Robust Object Recognition with Cortex-Like Mechanisms (2007, methodology): “This may come from the fact that we only sample the space of features and do not perform any clustering step like other approaches (including an earlier version of this system [ 34 ]).”
- Hierarchical learning : theory with applications in speech and vision (2009, background): “What sets the CBCL model apart from prior work, is that it was designed with much guidance from the neuroscience, neurophysiology and psychophysics literature, and has been shown to account for a range of recent anatomical and physiological data [87, 97, 96].”
- Realistic Modeling of Simple and Complex Cell Tuning in the HMAX Model, and Implications for Invariant Object Recognition in Cortex (2004, background): “The C2 → VTU connections are so far the only stage of the HMAX model where learning occurs (but see [22] for a method to learn S2 features with HMAX in the context of an object detection task).”
- Randomized Clustering Forests for Image Classification (2008, background): “[48], general low specific features are not as efficient as specific learned features for recognizing objects.”
- A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex (2005, background): “The introduction of learning, we believe, has been the key factor for the model to achieve a high-level of performance on natural images, see [Serre et al., 2002].”
- Object recognition with features inspired by visual cortex (2005, background): “This work appeared in a very preliminary form in [18].”