Graph-based semisupervised and manifold learning for image retrieval with SVM-based relevant feedback

Huu, Quynh Nguyen; Viet, Dung Cu; Thuy, Quynh Dao Thi; Quoc, Tao Ngo; Van, Canh Phuong

doi:10.3233/jifs-181237

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…Moreover, more complex query scenarios than the plain pool-based sampling used, like query synthesis [ 56 ] could also be beneficial. As regards the semi-supervised part, simple techniques like the integration of weights annotating the instances assessed as informative by the SSL part of the algorithm could further improve the overall accuracy of the combination scheme as suggested in [ 35 , 57 ].…”

Section: Discussionmentioning

confidence: 99%

Combination of Active Learning and Semi-Supervised Learning under a Self-Training Scheme

Fazakis

Kanas

Aridas

et al. 2019

Entropy

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One of the major aspects affecting the performance of the classification algorithms is the amount of labeled data which is available during the training phase. It is widely accepted that the labeling procedure of vast amounts of data is both expensive and time-consuming since it requires the employment of human expertise. For a wide variety of scientific fields, unlabeled examples are easy to collect but hard to handle in a useful manner, thus improving the contained information for a subject dataset. In this context, a variety of learning methods have been studied in the literature aiming to efficiently utilize the vast amounts of unlabeled data during the learning process. The most common approaches tackle problems of this kind by individually applying active learning or semi-supervised learning methods. In this work, a combination of active learning and semi-supervised learning methods is proposed, under a common self-training scheme, in order to efficiently utilize the available unlabeled data. The effective and robust metrics of the entropy and the distribution of probabilities of the unlabeled set, to select the most sufficient unlabeled examples for the augmentation of the initial labeled set, are used. The superiority of the proposed scheme is validated by comparing it against the base approaches of supervised, semi-supervised, and active learning in the wide range of fifty-five benchmark datasets.

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Section: Discussionmentioning

confidence: 99%

Combination of Active Learning and Semi-Supervised Learning under a Self-Training Scheme

Fazakis

Kanas

Aridas

et al. 2019

Entropy

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An overview of cluster-based image search result organization: background, techniques, and ongoing challenges

Tekli

2022

Knowl Inf Syst

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Efficient descriptors selection in automatic image retrieval using DENOL

Ignat

Luca

Pavaloi

et al. 2022

IFS

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A well-structured and indexed database alleviates the computing burden on large data. This paper describes groundwork for presenting the data in a compact, distinctive form, improving the procedures of applying keypoint detection algorithms to preprocess and reduce the relevant features of the images. Our method computes for an image a number of SURF keypoints in a given interval, by adapting the threshold related to the Hessian matrix blob detector. This type of approach allows selecting the level of detail to use in image description and gives us control over the computing time. We named this method DENOL (Descriptor Number On Limits) and tested it on images from two datasets, UCID and an original image database which we propose, IIT_DB. Very good retrieval results and a significantly reduced computing time are achieved.

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Graph-based semisupervised and manifold learning for image retrieval with SVM-based relevant feedback

Cited by 3 publications

References 35 publications

Combination of Active Learning and Semi-Supervised Learning under a Self-Training Scheme

Combination of Active Learning and Semi-Supervised Learning under a Self-Training Scheme

An overview of cluster-based image search result organization: background, techniques, and ongoing challenges

Efficient descriptors selection in automatic image retrieval using DENOL

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