A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback

By Yi Yang, Feiping Nie, Dong Xu, Jiebo Luo, Yueting Zhuang, Yunhe Pan. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012

401 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/9b505dd5459fb28f0136d3c63793b600042e6a94

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In Computer Vision and Pattern Recognition

Citations in the corpus

Pairwise Relationship Guided Deep Hashing for Cross-Modal Retrieval (2017, background): “Since data from different modalities may have strong semantic correlations, it is essential to support cross-modal retrieval (Song et al. 2013; Zhang and Li 2014; Lin et al. 2015; Yang et al. 2012; 2009) that returns relevant results of one modality when querying another modality, e.”
Exploit the Unknown Gradually: One-Shot Video-Based Person Re-identification by Stepwise Learning (2018, background): “graph-based semi-supervised learning [33] could possibly be adopted for one-shot person Re-ID, it is time-consuming to solve a linear system for each query.”
Multi-Class Active Learning by Uncertainty Sampling with Diversity Maximization (2015, methodology): “Semi-supervised learning has been widely applied to many applications with the appealing feature that it can use both labelled and unlabelled data (Yang et al. 2012; Zhu 2008).”
Graph Learning for Multiview Clustering (2018, neutral): “Besides with Gaussian function, there are several graph construction methods, such as local linear similarity graph [1], k-nearest neighbor graph [2], [3], local discriminant graph [4], [5], pairwise similarity graph [6], and graph learned with subspace clustering [7].”
Semantic Pooling for Complex Event Analysis in Untrimmed Videos (2016, background): “…make sense of the vast information contained in these videos has become an important challenge for the computer vision and multimedia communities [1], [2], [3], [4], and a lot of recent work has been devoted to this exciting ﬁeld which we generally refer as event analysis on untrimmed videos.”
SIFT Meets CNN: A Decade Survey of Instance Retrieval (2016, background): “ scene recognition [7] and object categorization [18], [19], in which the inverted index is replaced by the Support Vector Machine (SVM). The BoW model has been the state of the art among many others [20], [21], [22]. It has greatly advanced the research of instance retrieval in the past ten years, ”
Visual Classification by ℓ1-Hypergraph Modeling (2015, methodology): “In particular, hypergraph model can be an effective tool to comprehensively mine the complex sample relationships in visual data and thus achieved encouraging performance in many visual classification tasks [16], [23], [58].”
An Adaptive Semisupervised Feature Analysis for Video Semantic Recognition (2018, background): “They either involve maximization of clustering performance as quantified by some index [5]–[7] or considers the selection of features based on feature dependency and relevance [8]–[10] Although unsupervised feature selection methods are capable of handling unlabeled data, they ignore the discriminat”
An Overview of Cross-Media Retrieval: Concepts, Methodologies, Benchmarks, and Challenges (2017, background): “[93] propose to convert long-term feedback information into pairwise similar/dissimilar constraints to refine the vector representation of data.”
Learning to Predict Gradients for Semi-Supervised Continual Learning (2022, background): “Most modern deep learning based models follow this line of research [16], [17], [19], [20], [35], [36], [37], [38], [39], [40], [41].”
Cross-Modal Retrieval With CNN Visual Features: A New Baseline (2017, background): “[61] proposed a semi-supervised algorithm called ranking with local regression and global alignment to learn a robust”