Multiple feature hashing for real-time large scale near-duplicate video retrieval

By Jingkuan Song, Yi Yang, Zi-Liang Huang, Heng Tao Shen, Richang Hong. ACM Multimedia, 2011

327 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/348b0133bd5d0d885ce7e0f8e5d6bb394a03b2b2

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In Computer Vision and Pattern Recognition

Citations in the corpus

A Comprehensive Survey on Cross-modal Retrieval (2016, methodology): “Accordingly, multi-view hashing methods (e.g., CHMIS [101] and MFH [102]) leverage information contained in different features to learn more accurate hash codes.”
Large-Scale Supervised Multimodal Hashing with Semantic Correlation Maximization (2014, methodology): “MSH is also called multiple feature hashing (Song et al. 2011) or composite hashing (Zhang, Wang, and Si 2011), which aims at learning better codes by leveraging auxiliary views than unimodal hashing.”
Multimodal Abstractive Summarization for How2 Videos (2019, background): “With the abundance of videos online, there has been an increase in demand for efﬁcient ways to search and retrieve relevant videos (Song et al., 2011; Wang et al., 2012; Otani et al., 2016; Torabi et al., 2016).”
Query-driven iterated neighborhood graph search for large scale indexing (2012, methodology): “binary reconstruction [21], kernelized LSH [22], and shift kernel hashing [30], semi-supervised hashing [42], weaklysupervised hashing [25], non-metric hashing [26], multifeature hashing [38], spectral hashing [45], complementary hashing [46].”
Deep Networks With Large Output Spaces (2014, methodology): “…benchmarks Weston et al. (2011b) to several million in commercial search engines such as Google, Bing, Yahoo, etc. Duplicate video content identiﬁcation (Shang et al., 2010; Song et al., 2011; Zhao et al., 2007) and video recommendations are also large-scale problems with millions of classes.”
Optimized Cartesian K-Means (2014, background): “C V ] 16 M ay 2 01 4 Index Terms—Clustering, Cartesian product, Nearest neighbor search ✦”
Learning in high-dimensional multimedia data: the state of the art (2015, background): “64 bits 128 bits BRE [44] High low SSH [92] low low ITQ [23] fair fair KSH [53] High fair FastHash [50] High There are also some further developments extended from the standard hashing algorithms: Multiple Feature Hashing (MFH) [81] learns a group of hash functions to generate binary codes for multi”
Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection (2017, background): “Although some studies on duplication detection are available and effective in different tasks [15, 26, 50], these approaches are not applicable in our case where the duplication cannot be determined beforehand but is rather varied across post series over time.”
Linear cross-modal hashing for efficient multimedia search (2013, unknown): “Keywords Cross-modal, hashing, index, multimedia search”
Inter-media hashing for large-scale retrieval from heterogeneous data sources (2013, methodology): “Differently, multiple feature hashing (MFH) [27] learns a group of hash functions for multiple features by preserving the local structure information of each individual feature and also globally considering the local structures for all the features.”
Discriminative coupled dictionary hashing for fast cross-media retrieval (2014, methodology): “…two retrieval schemes, we compare with the state-of-the-art cross-media hashing methods CMSSH [3], CVH [9], MLBE [28], LCMH [30]; for the third retrieval scheme, we additionally compare with some uni-modal hashing approaches: SH [23], KLSH [8], AGH [11] and a multi-view hashing approach MFH [17].”
CompoundEyes: Near-duplicate detection in large scale online video systems in the cloud (2016, methodology): “Feature combination is a common approach in constructing discriminative video feature representations, typical examples including global and local features [2], [4], [5], [22], or spatial and temporal features [7], [9].”