Image annotation using metric learning in semantic neighbourhoods
Citations
5 citations
Cites methods from "Image annotation using metric learn..."
...Since JEC is the essential backend method for modern successful techniques [2, 4], we compare our results with JEC [1] and show that our method is robust under noisy labels....
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...K-nearest neighbour (or KNN) based methods [1, 2, 4] have been found to give some of the best results on the task of image annotation....
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...Most relevant KNN-based annotation methods are (i) JEC [1], which treats the annotation problem as retrieval and proposes a greedy algorithm for label transfer from neighbours, (ii) TagProp [4], a weighted KNN based method that transfers labels by taking weighted average of labels present among the neighbours, and (iii) 2PKNN [2], where a class-wise semantic neighbourhood is defined and only samples within this neighbourhood are used for annotation of unseen image....
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...Existing KNN-based methods [1, 2, 4] make an inherent assumption that labels present in the training set are reliable and correct, and hence can be directly...
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5 citations
Cites methods from "Image annotation using metric learn..."
...The third method is called “Two-step variant of a classic Knearest neighbor algorithm” (2pkkn) [10], which is a famous method in image annotation and it has two steps....
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5 citations
Cites methods from "Image annotation using metric learn..."
...To automate the keywords extraction process for images, a number of research works have been proposed with the concept of “automatic image annotation” [11], [25]– [27]....
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5 citations
Cites background from "Image annotation using metric learn..."
...…with noisy data (Settles, 2009), (Yan et al., 2011), (Fang and Zhu, 2012) or even non-active learning from noisy data in the multimedia domain (Chatzilari et al., 2012), (Raykar et al., 2010), (Yan et al., 2010), (Uricchio et al., 2013), (Verma and Jawahar, 2012), (Verma and Jawahar, 2013)....
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..., 2013), (Verma and Jawahar, 2012), (Verma and Jawahar, 2013)....
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References
4,433 citations
4,157 citations
"Image annotation using metric learn..." refers background or methods in this paper
...With this goal, we perform metric learning over 2PKNN by generalizing the LMNN [11] algorithm for multi-label prediction....
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...In such a scenario, (i) since each base distance contributes differently, we can learn appropriate weights to combine them in the distance space [2, 3]; and (ii) since every feature (such as SIFT or colour histogram) itself is represented as a multidimensional vector, its individual elements can also be weighted in the feature space [11]....
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...Our extension of LMNN conceptually differs from its previous extensions such as [21] in at least two significant ways: (i) we adapt LMNN in its choice of target/impostors to learn metrics for multi-label prediction problems, whereas [21] uses the same definition of target/impostors as in LMNN to address classification problem in multi-task setting, and (ii) in our formulation, the amount of push applied on an impostor varies depending on its conceptual similarity w.r.t. a given sample, which makes it suitable for multi-label prediction tasks....
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...Our metric learning framework extends LMNN in two major ways: (i) LMNN is meant for single-label classification (or simply classification) problems, while we adapt it for images annotation which is a multi-label classification task; and (ii) LMNN learns a single Mahalanobis metric in the feature space, while we extend it to learn linear metrics for multi- Image Annotation Using Metric Learning in Semantic Neighbourhoods 3 ple features as well as distances together....
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...For this purpose, we extend the classical LMNN [11] algorithm for multi-label prediction....
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2,365 citations
"Image annotation using metric learn..." refers background in this paper
...ESP Game contains images annotated using an on-line game, where two (mutually unknown) players are randomly given an image for which they have to predict same keyword(s) to score points [22]....
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2,037 citations
"Image annotation using metric learn..." refers methods in this paper
...To overcome this issue, we solve it by alternatively using stochastic sub-gradient descent and projection steps (similar to Pegasos [12])....
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...To address this, we implement metric learning by alternating between stochastic sub-gradient descent and projection steps (similar to Pegasos [12])....
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1,765 citations
"Image annotation using metric learn..." refers background in this paper
...translation models [13, 14] and nearest-neighbour based relevance models [1, 8]....
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...Corel 5K was first used in [14], and since then it has become a benchmark for comparing annotation performance....
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