Multiple-instance learning based decision neural networks for image retrieval and classification

Xu, Yeong-Yuh

doi:10.1016/j.neucom.2015.07.024

Cited by 31 publications

(17 citation statements)

References 33 publications

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“…Otherwise, it will yield a poor performance. Similar to this, Xu YY et al [4] propose a multiple-instance learning based decision neural network, which improves the query accuracy. But, this approach still shares the above weakness, which does not express the relationship of bags and instances.…”

Section: Introductionmentioning

confidence: 88%

“…We select 5 2  as the parameter. The parameter C and Gaussian kernel parameter r for SVM in LIBSVM are simply set to 1 and 4 2  , respectively.…”

Section: Evaluation On Sival Data Setmentioning

confidence: 99%

“…By measuring the similarities between the query images and database using the MIL, it selects the most similar image as an interested one. Through this process, we can broadly categorize the existing MIL based image retrieval approaches into three main groups: 1) Interested point based approaches [2][3][4]: These approaches first find an interested point that likely is the center of positive instances, and estimate whether each bag has an instance that is close to the center. In [2], Zhang Q et al are first to perform image retrieval with the MIL method.…”

Section: Introductionmentioning

confidence: 99%

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A New Multiple Instance Learning Algorithm based on Instance-Consistency

Wu¹

2017

IJPE

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Multiple-instance learning (MIL) has been successfully utilized in image retrieval. Existing approaches cannot select positive instances correctly from positive bags, which may result in low accuracy. Inspired by the characteristic that consistencies are always among instances and instances, and bags and instances, we propose a new algorithm called multiple instance learning based on instanceconsistency (MILIC) to mitigate this issue. First, we select potential positive instances effectively in every positive bag through the minimum cost of instance-consistency. Second, we use the L1-LR to select irrelevant instances from potential positive instances to further improve the retrieval efficiency. Then, we design a novel feature representation scheme based on the irrelevant potential positive instances to convert a bag into a single instance. Band on the feature representations, we finally conduct object-based image retrieval and image categorization by adopting the standard single-instance learning (SIL) strategy, such as the support vector machine (SVM), to verify the effectiveness of our proposal.

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Section: Introductionmentioning

confidence: 88%

“…We select 5 2  as the parameter. The parameter C and Gaussian kernel parameter r for SVM in LIBSVM are simply set to 1 and 4 2  , respectively.…”

Section: Evaluation On Sival Data Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Multiple Instance Learning Algorithm based on Instance-Consistency

Wu¹

2017

IJPE

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“…Many new applications violate the standard MI assumption, such as image classification [10] and text categorization [11]. Consequently, successful algorithms meet more general assumptions, see e.g., the hierarchy of Weidmann et al [12] or Foulds and Frank's taxonomy [13].…”

Section: Multi-instance Learningmentioning

confidence: 99%

A Probabilistic Bag-to-Class Approach to Multiple-Instance Learning

Møllersen

Hardeberg

Godtliebsen

2020

Data

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Multi-instance (MI) learning is a branch of machine learning, where each object (bag) consists of multiple feature vectors (instances)—for example, an image consisting of multiple patches and their corresponding feature vectors. In MI classification, each bag in the training set has a class label, but the instances are unlabeled. The instances are most commonly regarded as a set of points in a multi-dimensional space. Alternatively, instances are viewed as realizations of random vectors with corresponding probability distribution, where the bag is the distribution, not the realizations. By introducing the probability distribution space to bag-level classification problems, dissimilarities between probability distributions (divergences) can be applied. The bag-to-bag Kullback–Leibler information is asymptotically the best classifier, but the typical sparseness of MI training sets is an obstacle. We introduce bag-to-class divergence to MI learning, emphasizing the hierarchical nature of the random vectors that makes bags from the same class different. We propose two properties for bag-to-class divergences, and an additional property for sparse training sets, and propose a dissimilarity measure that fulfils them. Its performance is demonstrated on synthetic and real data. The probability distribution space is valid for MI learning, both for the theoretical analysis and applications.

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“…In this case, any visual content that displays a beach may be considered as positive image while negative images show another different visual content. Xu proposed an MIL extension of Neural Network for the retrieval and classification of images [43]. Maron and Ratan applied DD MIL approach for the classification of natural image scene by using different kinds of bag generators [44].…”

Section: Overview Of Multiple Instance Learning Approaches and Their mentioning

confidence: 99%

Twin Support Vector Machine for Multiple Instance Learning Based on Bag Dissimilarities

Tomar

Agarwal

2016

Advances in Artificial Intelligence

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In multiple instance learning (MIL) framework, an object is represented by a set of instances referred to as bag. A positive class label is assigned to a bag if it contains at least one positive instance; otherwise a bag is labeled with negative class label. Therefore, the task of MIL is to learn a classifier at bag level rather than at instance level. Traditional supervised learning approaches cannot be applied directly in such kind of situation. In this study, we represent each bag by a vector of its dissimilarities to the other existing bags in the training dataset and propose a multiple instance learning based Twin Support Vector Machine (MIL-TWSVM) classifier. We have used different ways to represent the dissimilarity between two bags and performed a comparative analysis of them. The experimental results on ten benchmark MIL datasets demonstrate that the proposed MIL-TWSVM classifier is computationally inexpensive and competitive with state-of-the-art approaches. The significance of the experimental results has been tested by using Friedman statistic and Nemenyi post hoc tests.

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Multiple-instance learning based decision neural networks for image retrieval and classification

Cited by 31 publications

References 33 publications

A New Multiple Instance Learning Algorithm based on Instance-Consistency

A New Multiple Instance Learning Algorithm based on Instance-Consistency

A Probabilistic Bag-to-Class Approach to Multiple-Instance Learning

Twin Support Vector Machine for Multiple Instance Learning Based on Bag Dissimilarities

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