Manifold-ranking based image retrieval

By Jingrui He, Mingjing Li, HongJiang Zhang, Hanghang Tong, Changshui Zhang. MULTIMEDIA '04, 2004

389 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a61414239dd76b5e936e492def93774cce96bfd7

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In Computer Vision and Pattern Recognition

Citations in the corpus

Multimodal Graph-Based Reranking for Web Image Search (2012, methodology): “In [36], He et al. adopt a graphbased method named manifold-ranking in image retrieval, and Yuan et al. [37] then apply the same algorithm to video annotation.”
Learning a Mahalanobis distance metric for data clustering and classification (2008, background): “Thus, supplying such a distance metric is highly problem-specific and determines the success or failure of the learning algorithm or the developed system [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13].”
Spectral regression: a unified subspace learning framework for content-based image retrieval (2007, background): “INTRODUCTION Content-Based Image Retrieval (CBIR) has attracted substantial interests as the volumes of image data have grown rapidly during the last decade [9, 10, 15, 21, 22, 27, 28].”
Generative Image Segmentation Using Random Walks with Restart (2008, methodology): “It has good performance on many other applications: Cross-model correlation discovery [8], Center-piece subgraph discovery [13], Content based image retrieval [12], Neighborhood formulation [14] and etc.”
Distance Metric Learning: A Comprehensive Survey (2006, methodology): “Distance Metric Learning: A Comprehensive Survey Liu Yang Advisor : Rong Jin Department of Computer Science and Engineering Michigan State University May 19, 2006 Contents”
Random walk with restart: fast solutions and applications (2008, methodology): “We use exactly the same method as in [14] to construct the weighted graph matrix W, which contains 5, 000 nodes and ≈ 774K edges – CoMMG This dataset is used in [25], which contains around 7,000 captioned images, each with about four captioned terms.”
Iteratively clustering web images based on link and attribute reinforcements (2005, neutral): “This result is also reported by He et al [10].”
Image retrieval: Ideas, influences, and trends of the new age (2008, positive): “In [He et al. 2004], it is conceived that image features reside on a manifold embedded in the Euclidean feature space.”
Unified Video Annotation via Multigraph Learning (2009, methodology): “These methods have already been successfully applied in image and video content analysis on account of their effectiveness and efficiency [ 12 ], [37], [48].”
A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback (2012, neutral): “These works include content-based image retrieval [15], [16], [40], audio retrieval [21], and video retrieval [12].”
Fast Random Walk with Restart and Its Applications (2006, methodology): “~ri = (1 − c)(I − cW̃)~ei = (1 − c)Q~ei (2) The relevance score defined by RWR has many good properties: compared with those pair-wise metrics, it can capture the global structure of the graph [14]; compared with those traditional graph distances (such as shortest path, maximum flow etc), it can cap”
Identifying remote protein homologs by network propagation (2005, methodology): “Finally, most recently, because the success of the rankprop algorithm, the authors of [ 32 ] have also applied the rankprop algorithm to content based image retrieval with iterative feedback, with state of the art results.”