Image annotation using metric learning in semantic neighbourhoods
Citations
33 citations
Cites background from "Image annotation using metric learn..."
...The CRM estimates the joint probability distribution of a set of words w = {w1 . . . wK} from a vocabulary of size V together with an image f represented as a set of feature vectors f = {~f1. . . ~fM}....
[...]
...…required so to achieve superior performance over models that utilise a full suite of feature types; and secondly, the standard default selection of kernels commonly used in the literature is sub-optimal, and it is much better to adapt the kernel choice based on the feature type and image dataset....
[...]
32 citations
Cites background from "Image annotation using metric learn..."
...Generally, previous researches for image annotation can be roughly categorized into three groups: generative models [25]–[27], discriminative models [23], [28] and nearest neighbor (NN) based models [10]–[12], [29], [30]....
[...]
26 citations
Cites background or methods or result from "Image annotation using metric learn..."
...HYP-2 Learning an optimal combination of kernels using the data itself, owing to its different geometry over the feature space, will outperform the standard (default) assignment of kernels to feature types often found in the literature [17,45]....
[...]
...The training and testing dataset splits for all three datasets are identical to previously published work [17,45]....
[...]
...All datasets are identical to those used in most recent image annotation publications [17,45], thereby permitting direct comparison....
[...]
...We use the identical subset of images as [45]....
[...]
...To fairly compare our model performance to previously published figures we use the identical feature set, parameter optimisation strategy and evaluation procedure of previous relevant work [17,26,45]....
[...]
25 citations
Cites background or methods from "Image annotation using metric learn..."
...But, for a fair comparison with other methods, we used the same feature set as in [20] and [21], without any claim of being the best feature set....
[...]
...0 238 - - - 2PKNN+ML [21] 44 46 45 191 53 27 35....
[...]
...The previous works [12, 20, 21], simply chose a fixed number of tags (5 tags) to annotate all test images....
[...]
...To the best of our knowledge, the best search-based work until now is 2PKNN [21] which uses a two-level multi-label metric learning to learn the weights of each feature and also the weight of each element of a single feature vector....
[...]
25 citations
Cites background from "Image annotation using metric learn..."
...much attention for image annotation and tag relevance estimation [3, 4, 5]....
[...]
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....
[...]
...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]....
[...]
...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....
[...]
...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....
[...]
...For this purpose, we extend the classical LMNN [11] algorithm for multi-label prediction....
[...]
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]....
[...]
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])....
[...]
...To address this, we implement metric learning by alternating between stochastic sub-gradient descent and projection steps (similar to Pegasos [12])....
[...]
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]....
[...]
...Corel 5K was first used in [14], and since then it has become a benchmark for comparing annotation performance....
[...]