Image annotation using metric learning in semantic neighbourhoods
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4 citations
Cites background or methods from "Image annotation using metric learn..."
...We compare with following benchmark methods: (a) Citation kNN [17], (b) MIMLSVM [19], (c) MildML [8], (d) SC2B and its variants (C2B and M-C2B) [15], (e) TagProp [7], and (f) 2PKNN [13]....
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..., L}, we follow the approach of metric learning using pair-wise comparisons [18, 4, 5, 13]....
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...For MIMLSVM [19], MildML [8], TagProp [7] and 2PKNN [13], we use publicly available codes....
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...While metric learning for single-instance data (single-label [5, 4, 18] or multi-label [7, 13]) is a well-studied topic, there have been few attempts that perform metric learning for multiinstance data....
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...We use the same train/test partitions for both the datasets as in [7, 13]....
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4 citations
Cites background from "Image annotation using metric learn..."
...Then it assigns to the image the top five most related tags by the nearest neighbors’ concept[7]....
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4 citations
Additional excerpts
...) using different classifiers (SVMs, [4], [5], [6], nearest neighbors [7], [8], etc....
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4 citations
Cites background or methods from "Image annotation using metric learn..."
...First, we compare the proposed 2PKNN-GSR method with the nearest neighbor based methods [6, 14, 23]....
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...In this section, we make use of 2PKNN [23] to obtain the relevance between the labels and testing images....
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...The first step of this framework is to obtain the relevance between the labels and the testing images using the traditional 2PKNN method [23]....
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...recently proposed 2PKNN [23], which takes advantage of image-to-label and image-to-image similarities to respectively the “class-imbalance” and the “weak-labeling” issues at the same time....
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...According to [23], we can see that the 2PKNN method can solve the problem of weaklabeling and class-imbalance....
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4 citations
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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