Correlative multi-label video annotation

By Guo-Jun Qi, Xiansheng Hua, Yong Rui, Jinhui Tang, Tao Mei, HongJiang Zhang. ACM Multimedia, 2007

504 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ae11fe0736e1fed743b6ad389d3843c6d682a27d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Multi-task deep neural network for multi-label learning (2013, background): “Multi-Label Learning Multi-label learning ([7], [9], [10], [11]) is a special kind of supervised learning where each instance can belong to multiple classes.”
Feature selection for multi-label naive Bayes classification (2009, background): “[28] studied the problem of automatic multi-label video annotation.”
Lift: Multi-Label Learning with Label-Specific Features (2011, background): “…and related communities, which has been successfully applied to diverse applications such as bioinformatics [1], [5], multimedia contents analysis including image [3], [4], [49], audio [27], [34], [42], and video [35], [41], [48], web mining [25], [33], [36], information retrieval [52], [54], etc.”
Transductive Multilabel Learning via Label Set Propagation (2013, background): “…problem has been studied by a lot of researchers and many algorithms have been developed to solve different real-world application tasks, such as text categorization [8], [13], [20], [24], [28], [31], bioinformatics [10], [34], scene classification [4], image or video annotation [27].”
Binary relevance for multi-label learning: an overview (2018, background): “Generally, multi-label objects widely exist in real-world applications including information retrieval [5], bioinformatics [6], multimedia content annotation [7], web mining [8], etc.”
Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks (2015, result): “However, video semantics often share some commonality due to the strong correlations between different semantic categories, which have been observed in previous studies [37], [54], [55].”
Multi-label learning by exploiting label dependency (2010, background): “To name a few, in text categorization, each document may belong to several topics, such as Shanghai World Expo, economics and even volunteers [14, 19]; In bioinformatics, each gene may be associated with a number of functional classes, such as metabolism, transcription and protein synthesis [7]; In ”
Efficient multi-label ranking for multi-class learning: Application to object recognition (2009, background): “In multi-label ranking, we aim to learn K classification functions fk(x) : Rd → R, k = 1, . . . , K, one for each class, such that for any example x, fk(x) is larger than fl(x) when x belongs to class ck and does not belong to class cl.”
Social Network Data Analytics (2011, background): “[81] Ah Chung Tsoi, Gianni Morini, Franco Scarselli, Markus Hagenbuchner, and Marco Maggini.”
Towards cross-category knowledge propagation for learning visual concepts (2011, background): “It is worth noting that the previous work in correlative multi-label (CML) classifiers [7] model the label correlation.”
Mining Multi-label Data (2010, background): “Recently, the issue of learning from multi-label data has attracted significant attention from a lot of researchers, motivated from an increasing number of new applications, such as semantic annotation of images [1, 2, 3] and video [4, 5], functional genomics [6, 7, 8, 9, 10], music categorization i”
Deep Adaptive Image Clustering (2017, background): “Many applications such as content-based image annotation [19, 20, 22, 23] and image retrieval [12, 24, 34] can be viewed as different instances of image clustering.”
On the Consistency of Multi-Label Learning (2011, unknown): “…called evaluation criteria ), e.g., ranking loss , hamming loss , one-error , coverage and average precision (Schapire and Singer, 2000; Zhang and Zhou, 2006); accuracy , precision , recall and F 1 (Godbole and Sarawagi, 2004; Qi et al., 2007); subset accuracy (Ghamrawi and McCallum, 2005); etc.”
Multi-instance multi-label learning (2008, background): “classification [11], image and video annotation [37, 47 ], bioinformatics [7,12,13,21,”