Discriminative Subsequence Mining for Action Classification

By Sebastian Nowozin, Gökhan H. Bakir, Koji Tsuda. 2007

220 citations recorded by the source corpus.

Subfields: Artificial Intelligence, 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 Artificial Intelligence

1. Luc Van Gool
2. Kristen Grauman
3. Li Fei-Fei
4. Trevor Darrell
5. Larry S. Davis
6. Dacheng Tao
7. Greg Mori
8. Nuno Vasconcelos

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Ming-Hsuan Yang
3. Dacheng Tao
4. Xiaogang Wang
5. Jitendra Malik
6. Kristen Grauman
7. Trevor Darrell
8. Andrew Zisserman

Citations in the corpus

• Video Representation Learning Using Discriminative Pooling (2018, methodology): “[31] represent videos as sequences of discretized spatio-temporal sets and reduces the recognition task into a max-gain sequence finding problem on these sets using an LPBoost classifier.”
• Temporal Localization of Actions with Actoms (2013, unknown): “We describe the process used to acquire training actom annotations in Section 3.3.”
• Human activities recognition using depth images (2013, unknown): “Copyright 2013 ACM 978-1-4503-2404-5/13/10 ...$15.00.”
• Human Action Segmentation and Recognition Using Discriminative Semi-Markov Models (2011, methodology): “1%) is comparable to similar methods (e.g. SVM of Dollar et al. 2005; Nowozin et al. 2007) reported in literature, while our BMRM-SMM (95.”
• Understanding Video Events: A Survey of Methods for Automatic Interpretation of Semantic Occurrences in Video (2009, methodology): “Most of the popular boosting methodologies are used to construct event models in the literature of video event understanding including: AdaBoost [32, 80] and LPBoost [81].”
• Learning a hierarchy of discriminative space-time neighborhood features for human action recognition (2010, background): “To identify informative local video descriptors, boosting [12, 6], PageRank [18], and item-set mining [23, 9] have all been explored.”
• Recognizing actions by shape-motion prototype trees (2009, methodology): “Classifiers commonly used for action recognition include NN/k-NN classifiers [2] [6] [19] [25] [30] [31], Support Vector Machine (SVM) classifiers [5] [9] [12] [16] [23] [24], boosting-based classifiers [8] [13] [21], Hidden Markov Model (HMM) [7].”
• Recognising action as clouds of space-time interest points (2009, unknown): “From top to bottom: example frames from KTH dataset, WEIZMANN dataset and WEIZMANN robustness test dataset.”
• A survey on activity recognition and behavior understanding in video surveillance (2013, methodology): “[122] used a sequential representation, which retained the temporal order.”
• Modeling the Temporal Extent of Actions (2010, background): “In [ 12 ], Nowozin et al . present an algorithm which searches for discriminative subsequence patterns in videos.”
• Hidden Part Models for Human Action Recognition: Probabilistic versus Max Margin (2011, background): “Nowozin et al. [13] find discriminative subsequences in videos for action recognition.”
• Human Action Recognition by Semilatent Topic Models (2009, unknown): “Nowozin et al. [42] first detect local interest points, and then learn a set of discriminative subsequences for action classification by exploiting the sequence mining techniques from data mining.”