Semantic manifold learning for image retrieval

By Yen-Yu Lin, Tyng-Luh Liu, Hwann-Tzong Chen. 2005

117 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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

1. Dacheng Tao
2. Yi Yang
3. Luc Van Gool
4. Heng Tao Shen
5. Deng Cai
6. Jingkuan Song
7. Xiaogang Wang
8. Qi Tian

In Media Technology

1. Xiaofei He
2. Deng Cai
3. Jiawei Han
4. Heng Tao Shen
5. Richang Hong
6. Zi-Liang Huang
7. Yi Yang
8. Kevin Chen-Chuan Chang

Citations in the corpus

• A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (2017, background): “(4) AgLPP [79] anchor graph,W ¼ ZL 1ZT , Lkk ¼ P Zik, Zik ¼ Ks ðXi;UkÞP j Ks ðXi;UjÞ a ¼ argmin aT ULUT a aT UDUT a LGRM [98] KNN,Wij ¼ exp kXi Xjk2 2t2 y ¼ argmin y T ðLleþmLgÞy yT y ARE [88] KNN,Wij ¼ exp r2ðXiXjÞ t ,WAREij ¼ g; Xi 2 Fþ&Xj 2 Fþ 1;LðXiÞ 6¼ LðXjÞ 0; otherwise 8< : a ¼ argmax a T XLA”
• Graph Embedding Techniques, Applications, and Performance: A Survey (2017, background): “, Augmented Relation Embedding (ARE) [43] augments network with content based features for images and modifies the graph-Laplacian to capture such information.”
• Multiple feature hashing for real-time large scale near-duplicate video retrieval (2011, background): “As demonstrated in [12, 15, 26, 27, 34, 35], it is beneficial to exploit the local structure of the training data to infer an accurate and compact representation, especially for multimedia data.”
• Semi-supervised Discriminant Analysis (2007, methodology): “To simulate such environment, we use five-fold cross validation to evaluate the algorithms which is also adopted in the paper [15].”
• Inter-media hashing for large-scale retrieval from heterogeneous data sources (2013, background): “Previous research works have shown the benefit and success of exploiting the local structural information within a single data type (especially for high-dimensional multimedia data) in various applications, ranging from feature selection to annotation and retrieval [22, 25, 32, 35, 36, 34].”
• Image retrieval: Ideas, influences, and trends of the new age (2008, methodology): “Recently, a manifold learning technique to capture user preference over a semantic manifold from RF is proposed in [Lin et al. 2005].”
• Spectral regression: a unified subspace learning framework for content-based image retrieval (2007, methodology): “To simulate such environment, we use five-fold cross validation to evaluate the algorithms which is also adopted in the paper [15].”