A practical SVM-based algorithm for ordinal regression in image retrieval

“…To date, it has been widely applied to facial recognition [9], information retrieval [10], music recommendation [11], and gene expression analysis [7]. According to the availability of training labels, the existing OR work can be broadly classified into two categories: 1) supervised OR classification approaches, where all sample labels are known to train the classifier and 2) semisupervised OR classification approaches, where the classifier is learnt on a few labeled samples and a large number of unlabeled samples.…”

Section: Related Work On Ordinal Regressionmentioning

confidence: 99%

A Maximum Margin Approach for Semisupervised Ordinal Regression Clustering

Xiao

Zhang

2016

IEEE Trans. Neural Netw. Learning Syst.

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Ordinal regression (OR) is generally defined as the task where the input samples are ranked on an ordinal scale. OR has found a wide variety of applications, and a great deal of work has been done on it. However, most of the existing work focuses on supervised/semisupervised OR classification, and the semisupervised OR clustering problems have not been explicitly addressed. In real-world OR applications, labeling a large number of training samples is usually time-consuming and costly, and instead, a set of unlabeled samples can be utilized to set up the OR model. Moreover, although the sample labels are unavailable, we can sometimes get the relative ranking information of the unlabeled samples. This sample ranking information can be utilized to refine the OR model. Hence, how to build an OR model on the unlabeled samples and incorporate the sample ranking information into the process of improving the clustering accuracy remains a key challenge for OR applications. In this paper, we consider the semisupervised OR clustering problems with sample-ranking constraints, which give the relative ranking information of the unlabeled samples, and put forward a maximum margin approach for semisupervised OR clustering ( [Formula: see text]SORC). On one hand, [Formula: see text]SORC seeks a set of parallel hyperplanes to partition the unlabeled samples into clusters. On the other hand, a loss function is put forward to incorporate the sample ranking information into the clustering process. As a result, the optimization function of [Formula: see text]SORC is formulated to maximize the margins of the closest neighboring clusters and meanwhile minimize the loss associated with the sample-ranking constraints. Extensive experiments on OR data sets show that the proposed [Formula: see text]SORC method outperforms the traditional semisupervised clustering methods considered.

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“…Without loss of generality, we choose the DCT ordinal feature proposed in [17] to build the feature space. In the past, the ordinal measure [4] was widely adopted for applications in image/video retrieval [36] and copy detection [17], [18]. The DCT ordinal feature is particularly suitable for efficient image copy detection over the Internet, since it can be applied to compressed image formats (such as JPEG).…”

Section: Feature Space Employedmentioning

confidence: 99%

A New Approach to Image Copy Detection Based on Extended Feature Sets

Hsiao

Chen

Chien

et al. 2007

IEEE Trans. on Image Process.

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Abstract-Conventional image copy detection research concentrates on finding features that are robust enough to resist various kinds of image attacks. However, finding a globally effective feature is difficult and, in many cases, domain dependent. Instead of simply extracting features from copyrighted images directly, we propose a new framework called the extended feature set for detecting copies of images. In our approach, virtual prior attacks are applied to copyrighted images to generate novel features, which serve as training data. The copy-detection problem can be solved by learning classifiers from the training data, thus, generated. Our approach can be integrated into existing copy detectors to further improve their performance. Experiment results demonstrate that the proposed approach can substantially enhance the accuracy of copy detection.

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A practical SVM-based algorithm for ordinal regression in image retrieval

Cited by 9 publications

References 0 publications

Ordinal Regression Methods: Survey and Experimental Study

Ordinal Regression Methods: Survey and Experimental Study

A Maximum Margin Approach for Semisupervised Ordinal Regression Clustering

A New Approach to Image Copy Detection Based on Extended Feature Sets

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