Image annotation using metric learning in semantic neighbourhoods
Citations
92 citations
Cites background from "Image annotation using metric learn..."
...It can conduct multi-label dictionary learning in input feature space and partial-identical label embedding in output label space, simultaneously....
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...(Corresponding author: Xiao-Yuan Jing.)...
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...In addition, MLDL specially designs the label consistency regularization term for multi-label dictionary learning to enhance the discriminability of learned dictionary....
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76 citations
Cites background from "Image annotation using metric learn..."
...In addition, we found many papers that take into consideration special forms of data, including time series [54], structured [31], [55]–[59], multilabel, multiview, and bags [60]–[65]....
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76 citations
74 citations
Cites methods from "Image annotation using metric learn..."
...…[18] extend CRM to sparse kernel learning using a multinomial function for countbased features; alternative techniques, model the probability of concepts using parametric approaches such as gaussian mixture models [19], [20], Latent Dirichlet Allocation [21] and translation-based models [22]....
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70 citations
Cites background or methods or result from "Image annotation using metric learn..."
...We use three datasets popular in the image annotation task [5, 8, 11, 19]....
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...Once we have learned all the classifiers, predicting labels for a new image becomes several times faster than the NN-based models [5, 8, 11, 19]....
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...Hence this has emerged as an important research area during the last decade [2, 5, 6, 8, 11, 19, 23]....
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...Among the image annotation models being proposed in the past, generative or nearestneighbour (NN)-based models [5, 8, 11, 19, 23] have particularly been shown to be successful for large vocabulary datasets such as Corel-5k [4], ESP Game [20] and IAPRTC-12 [7]....
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...We use the same evaluation criteria as being used by previous methods [6, 8, 11, 19, 23]....
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References
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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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