Scene Classification Via pLSA

By Anna Bosch, Andrew Zisserman, Xavier Muñoz. 2006

876 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

Citations in the corpus

Large scale visual recommendations from street fashion images (2014, methodology): “We performed extensive experiments with texture features such Gabor Wavelets [10], Textons [12], Dense-SIFT[2] and found them to be unstable for texture classiﬁ-cation in clothing.”
Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions (2011, methodology): “We also compare them to the algorithm described by Bosch et al. (2006), which reﬂects the performance of a conventional “bag-of-word” (BoW) approach.”
Large scale mining and retrieval of visual data in a multimodal context (2008, methodology): “) based on a bag-of-features approach [Bosch et al., 2006] and label it using the textual meta-data rather than linking it to Wikipedia.”
A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation (2009, positive): “Probabilistic topic models, originally developed for text analysis [8, 12], have been adapted and extended successfully for many image-understanding problems [3, 6, 9–11, 16, 23, 24].”
Multiple kernels for object detection (2009, neutral): “Histogram of oriented edges (Phog180, Phog360) [7, 3].”
Performance evaluation of local colour invariants (2009, neutral): “Varma and Zisserman [40] modelled texture appearance by accumulation of the Gaussian-based MR8 filterbank .”
A Comparative Study of Bag-of-Words and Bag-of-Topics Models of EO Image Patches (2015, methodology): “In [9], the authors used the topics obtained by pLSA in combination with a nearest neighbor classifier for scene classification.”
Exploring Context and Content Links in Social Media: A Latent Space Method (2012, methodology): “These algorithms have also been applied to multimedia domain for problems such as indexing and retrieval [7], [17], [16], [29].”
DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo (2010, neutral): “This could have implications beyond stereo reconstruction because dense computation of image descriptors is fast becoming an important technique in other fields, such as object recognition [ 7 ], [18].”
Towards Reversal-Invariant Image Representation (2017, methodology): “First let us assume that the assignment of descriptor scale is finished, which fits the case of dense sampling (Bosch et al. 2006) where all the descriptors have the same, fixed window size.”
Incorporating visual adjectives for image classification (2016, background): “Particularly, in image classification, it is also suggested to densely extract descriptors from a regular grid on the image [8].”
Automatic landslide detection from remote-sensing imagery using a scene classification method based on BoVW and pLSA (2013, methodology): “Previously, both grey descriptors (Lowe 2004; Lazebnik, Schmid, and Ponce 2005) and colour descriptors (Abdel-Hakim and Farag 2006; Bosch, Zisserman, and Munoz 2006) have been used to represent images.”
Scene Classification Using a Hybrid Generative/Discriminative Approach (2008, background): “This paper is an expanded version of that in [4].”
A Visual Vocabulary for Flower Classification (2006, methodology): “There has been much recent success in using ‘bag of features’ or ‘bag of visual words’ models for object and scene classification [1, 3, 5, 7, 14, 15, 17].”
Which is the best way to organize/classify images by content? (2007, methodology): “[33] Implicit with the method Bag-of-words and pLSA Dense SIFT on a regular grid – concentric patches allow scale variation Up to 13: the same as in [34]”
Simultaneous image classification and annotation (2009, positive): “They reduce the error of Fei-Fei and Perona, 2005 by at least 10% on both data sets, and even more for Bosch et al., 2006.”
Scene Classification Based on the Multifeature Fusion Probabilistic Topic Model for High Spatial Resolution Remote Sensing Imagery (2015, methodology): “widely applied in the natural image scene analysis field [26]– [32].”