Random Forest for Image Annotation

Fu, Hao; Zhang, Qian; Qiu, Guoping

doi:10.1007/978-3-642-33783-3_7

Cited by 54 publications

(24 citation statements)

References 32 publications

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“…It can be seen that our method consistently improves performance over the conventional one-vs-rest SVM. Also, it performs comparable or better than even the recently proposed annotation methods such as [6,8,23] (except for IAPRTC-12 dataset where its performance is inferior only to the best results of [8]). We also compare with two SVM-based models [1,9].…”

Section: Discussionmentioning

confidence: 83%

“…We use the same evaluation criteria as being used by previous methods [6,8,11,19,23]. Given a new sample, first we compute the score for each label using the corresponding classifier, and then assign it the five top-scoring labels.…”

Section: Discussionmentioning

confidence: 99%

“…This has applications in several tasks such as image retrieval, object recognition, robot navigation, etc. Hence this has emerged as an important research area during the last decade [2,5,6,8,11,19,23]. In annotation datasets with large vocabularies of few hundred or more labels, there exist three practical issues: (a) Incomplete-labeling: The training samples are not exhaustively tagged with all relevant labels from vocabulary.…”

Section: Introductionmentioning

confidence: 99%

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Exploring SVM for Image Annotation in Presence of Confusing Labels

Verma¹,

Jawahar²

2013

Procedings of the British Machine Vision Conference 2013

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We address the problem of automatic image annotation in large vocabulary datasets. In such datasets, for a given label, there could be several other labels that act as its confusing labels. Three possible factors for this are (i) incomplete-labeling ("cars" vs. "vehicle"), (ii) label-ambiguity ("flowers" vs. "blooms"), and (iii) structural-overlap ("lion" vs. "tiger"). While previous studies in this domain have mostly focused on nearest-neighbour based models, we show that even the conventional one-vs-rest SVM significantly outperforms several benchmark models. We also demonstrate that with a simple modification in the hinge-loss of SVM, it is possible to significantly improve its performance. In particular, we introduce a tolerance-parameter in the hinge-loss. This makes the new model more tolerant against the errors in the classification of samples tagged with confusing labels as compared to other samples. This tolerance parameter is automatically determined using visual similarity and dataset statistics. Experimental evaluations demonstrate that our method (referred to as SVM with Variable Tolerance or SVM-VT) shows promising results on the task of image annotation on three challenging datasets, and establishes a baseline for such models in this domain.

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Section: Discussionmentioning

confidence: 83%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploring SVM for Image Annotation in Presence of Confusing Labels

Verma¹,

Jawahar²

2013

Procedings of the British Machine Vision Conference 2013

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“…As noted by [9,3,38], [15]'s training complexity is quadratic, O(n 2 ), where n is the number of training images. Since it relies on sophisticated training procedures and per tag optimizations, it is not scalable on large datasets.…”

Section: Computational Complexitymentioning

confidence: 99%

NMF-KNN: Image Annotation Using Weighted Multi-view Non-negative Matrix Factorization

Kalayeh

Idrees

Shah

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

128

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The real world image databases such as Flickr are characterized by continuous addition of new images. The recent approaches for image annotation, i.e. the problem of assigning tags to images, have two major drawbacks. First, either models are learned using the entire training data, or to handle the issue of dataset imbalance, tag-specific discriminative models are trained. Such models become obsolete and require relearning when new images and tags are added to database. Second, the task of feature-fusion is typically dealt using ad-hoc approaches. In this paper, we present a weighted extension of Multi-view Non-negative Matrix Factorization (NMF) to address the aforementioned drawbacks. The key idea is to learn query-specific generative model on the features of nearest-neighbors and tags using the proposed NMF-KNN approach which imposes consensus constraint on the coefficient matrices across different features. This results in coefficient vectors across features to be consistent and, thus, naturally solves the problem of feature fusion, while the weight matrices introduced in the proposed formulation alleviate the issue of dataset imbalance. Furthermore, our approach, being query-specific, is unaffected by addition of images and tags in a database. We tested our method on two datasets used for evaluation of image annotation and obtained competitive results.

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“…Thus each image is annotated with the n most relevant labels (usually, as in this paper, the results are obtained using n = 5). Then, the results are reported as mean precision P and mean recall R over the Previously reported results ML CRM [14] InfNet [19] NPDE [27] MBRM [4] SML [2] TGLM [17] GS [28] JEC-15 [9] TagProp σRK [9] TagProp σSD [9] RF-opt [5] KSVM-VT [26] 2PKNN [25] TagProp σML [9] 2PKNN ML [25] Our best result ground-truth labels; N+ is often used to denote the number of labels with non-zero recall value. Note that each image is forced to be annotated with n labels, even if the image has fewer or more labels in the ground truth.…”

Section: Evaluation Measuresmentioning

confidence: 99%

A Cross-media Model for Automatic Image Annotation

Ballan

Uricchio

Seidenari

et al. 2014

Proceedings of International Conference on Multimedia Retrieval

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Automatic image annotation is still an important open problem in multimedia and computer vision. The success of media sharing websites has led to the availability of large collections of images tagged with human-provided labels. Many approaches previously proposed in the literature do not accurately capture the intricate dependencies between image content and annotations. We propose a learning procedure based on Kernel Canonical Correlation Analysis which finds a mapping between visual and textual words by projecting them into a latent meaning space. The learned mapping is then used to annotate new images using advanced nearest-neighbor voting methods. We evaluate our approach on three popular datasets, and show clear improvements over several approaches relying on more standard representations.

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Random Forest for Image Annotation

Cited by 54 publications

References 32 publications

Exploring SVM for Image Annotation in Presence of Confusing Labels

Exploring SVM for Image Annotation in Presence of Confusing Labels

NMF-KNN: Image Annotation Using Weighted Multi-view Non-negative Matrix Factorization

A Cross-media Model for Automatic Image Annotation

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