Linear spatial pyramid matching using sparse coding for image classification

By Jianchao Yang, Kai Yu, Yihong Gong, Thomas S. Huang. 2009

3,226 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

ImageNet Large Scale Visual Recognition Challenge (2014, background): “5 - ◦ - - University of Isfahan Fatemeh Shafizadegan, Elham Shabaninia (Yang et al., 2009)”
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition (2014, methodology): “SPP has long been a key component in the leading and competition-winning systems for classi-ﬁcation ( e.g ., [17], [18], [19]) and detection ( e.g ., [20]) before the recent prevalence of CNNs.”
Visualizing and Understanding Convolutional Networks (2013, neutral): “[17] 73.”
Squeeze-and-Excitation Networks (2017, background): “Exploiting such information is prevalent in feature engineering work [35, 38, 49].”
A Joint Model of Language and Perception for Grounded Attribute Learning (2012, background): “, 2010) shows that these hand-designed features are equivalent to a type of match kernel that performs similarly to sparse coding (Yang et al., 2009; Yu & Zhang, 2010) and deep networks (Lee et al.”
Recent advances in convolutional neural networks (2015, background): “After several convolutional and pooling layers, there may be one or more fully-connected layers which aim to perform high level reasoning [8–10].”
Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos (2016, background): “while keeping others fixed to achieve a suboptimal solution which is already shown effective in different applications [80].”
Convolutional neural networks applied to house numbers digit classification (2012, methodology): “Lp-pooling has been used previously in [6, 16] and a theoretical analysis of this method is described in [1].”
Emergence of Complex-Like Cells in a Temporal Product Network with Local Receptive Fields (2010, background): “There are several system of this class (Lazebnik et al., 2006; Pinto et al., 2008, 2009; Serre et al., 2007; Yang et al., 2009; Lee et al., 2009; Yu et al., 2009) but speciﬁcally convolutional nets achieve 67% (Jarrett et al., 2009).”
Deep Convolutional Ranking for Multilabel Image Annotation (2013, neutral): “For example, sparse coding [33, 36], Fisher vectors [28], and VLAD [18] have been proposed to reduce the quantization error of “bag of words”-type features.”
Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks (2013, methodology): “This was noticed earlier in [8] as well as [33].”
Online Learning for Matrix Factorization and Sparse Coding (2009, positive): “The linear decomposition of a signal using a few atoms of a learned dictionary instead of a predeﬁned one—based on wavelets (Mallat, 1999) for example—has recently led to state-of-the-art results in numerous low-level signal processing tasks such as image denoising (Elad and Aharon, 2006; Mairal et ”
Distributed low rank approximation of implicit functions of a matrix (2016, methodology): “We ran several experiments to test the performance of our algorithm in different applications, including Gaussian random Fourier features [10], P-norm pooling [13] (square-root pooling [26]) and robust PCA.”
The Pascal Visual Object Classes Challenge: A Retrospective (2014, methodology): “For example the winning NEC/UIUC entry in 2009 used local coordinate coding (LCC, Yang et al 2009) to replace the vector quantisation step, so that a feature vector could be represented by more than one template.”
Learning mid-level features for recognition (2010, methodology): “[31] on the Scenes is not surprising since their baseline accuracy for the method in [12] is also lower, which they attributed to implementation differences.”