Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval
By Dacheng Tao, Xiaoou Tang, Xuelong Li, Xindong Wu. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006
960 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/38dede2e0361ba109b8e885e4e910752f6a08bf7
How researchers see this paper
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
- Weakly supervised target detection in remote sensing images based on transferred deep features and negative bootstrapping (2015, methodology): “To weaken the influence of negatives selection caused by randomly sampling, model averaging strategy was employed in some methods (Natsev et al. 2005; Tao et al. 2006) by combining multiple classifiers that were trained with randomly sampled negatives multiple times, but the effectiveness was still…”
- Multi-view hypergraph learning by patch alignment framework (2013, methodology): “It is proposed as a powerful analysis and development tool for dimensionality reduction.”
- 3-D Object Retrieval and Recognition With Hypergraph Analysis (2012, background): “Visual analyses [17]–[22] have shown its superiority in multimedia applications.”
- A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches (2012, neutral): “, Asymmetric Bagging [101] and QuasiBagging [100].”
- A survey of multiple classifier systems as hybrid systems (2014, neutral): “Support Vector Machines) locally presented in [93,94].”
- A Survey of Predictive Modeling on Imbalanced Domains (2016, methodology): “Namely, it was combined with boosting [Seiffert et al. 2010] and bagging [Chang et al. 2003; Tao et al. 2006; Wang and Yao 2009; Wallace et al. 2011] and was applied to both classes in random forests in a method named Balanced Random Forest (BRF) [Chen et al. 2004].”
- Good Practice in Large-Scale Learning for Image Classification (2014, methodology): “Note that more sophisticated strategies could also be used, as in [70].”
- Unsupervised Deep Hashing with Similarity-Adaptive and Discrete Optimization (2018, methodology): “HASHING has been recognized as an effective technique for large-scale retrieval [2], [9], [11], [39], [44], [45], [52].”
- A comprehensive review of current local features for computer vision (2008, background): “They are being applied widely in computer vision research, for such tasks as image retrieval [41,63,73,74,78], image registration [5], object recognition [9,10,30–32, There are two different ways of utilizing local features in applications: (i) traditional utilization, which involves the following…”
- Inverse random under sampling for class imbalance problem and its application to multi-label classification (2012, methodology): “However, other combining rules such as mean, product and average are also used in bagging [27,21,23].”
- Content-Based Image Retrieval in Radiology: Current Status and Future Directions (2010, methodology): “the Accio! system(52) or the retrieval machine can directly optimize the parameters of the similarity model via statistical learning as in.(51,54) Either way, the requirement of a fixed set of labels is removed since the labels are user-specific and sessiondependent.”
- Feature Selection Based on Structured Sparsity: A Comprehensive Study (2017, background): “, principal component analysis (PCA)] [8]–[11] or compression (e.”
- Efficient HOG human detection (2011, methodology): “In each subimage, a 3780-dimensional HOG feature vector is extracted and SVM classifier [12,8] is then used to make a binary decision: human or non-human.”