Nuclear norm regularized convolutional Max Pos@Top machine

Li, Qinfeng; Zhou, Xiaofeng; Gu, Anhui; Li, Zonghua; Liang, Ru‐Ze

doi:10.1007/s00521-016-2680-2

Cited by 23 publications

(8 citation statements)

References 40 publications

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“…In the future, we will also consider using the proposed method to other applications, such as integrated circuit design [41,42], software engineering [14,13,12], network measurement [3,4,2], commuter vision [44,43,5,35,27], medical imaging [23,29,17,34,22,33,10], etc. We will also consider to use some other loss function to learn the parameters of the CNN and the classifier to optimize the multivariate performance measures [36,24,21,26].…”

Section: Discussionmentioning

confidence: 99%

Cross-model convolutional neural network for multiple modality data representation

Wang

Cui

et al. 2017

Neural Comput & Applic

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A novel data representation method of convolutional neural network (CNN) is proposed in this paper to represent data of different modalities. We learn a CNN model for the data of each modality to map the data of different modalities to a common space, and regularize the new representations in the common space by a cross-model relevance matrix. We further impose that the class label of data points can also be predicted from the CNN representations in the common space. The learning problem is modeled as a minimization problem, which is solved by an augmented Lagrange method (ALM) with updating rules of Alternating direction method of multipliers (ADMM). The experiments over benchmark of sequence data of multiple modalities show its advantage.

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Section: Discussionmentioning

confidence: 99%

Cross-model convolutional neural network for multiple modality data representation

Wang

Cui

et al. 2017

Neural Comput & Applic

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“…We measure the retrieval performance by the positive at top (Pos@Top). The usage of this performance measure is motivated by the works of Liang et al [15,11]. The works of Liang et al [15,11] show that the Pos@Top is a robust and parameter-free performance measure, which is suitable for most database retrieval problems.…”

Section: Methodsmentioning

confidence: 99%

“…The usage of this performance measure is motivated by the works of Liang et al [15,11]. The works of Liang et al [15,11] show that the Pos@Top is a robust and parameter-free performance measure, which is suitable for most database retrieval problems. Following the works of Liang et al [15,11], we adapt this performance measure to evaluate the results of the image retrieval problem in our experiments.…”

Section: Methodsmentioning

confidence: 99%

“…The works of Liang et al [15,11] show that the Pos@Top is a robust and parameter-free performance measure, which is suitable for most database retrieval problems. Following the works of Liang et al [15,11], we adapt this performance measure to evaluate the results of the image retrieval problem in our experiments. The retrieval results of different methods are reported in Table 2.…”

Section: Methodsmentioning

confidence: 99%

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A Novel Image Tag Completion Method Based on Convolutional Neural Transformation

Geng¹,

Zhang

et al. 2017

Lecture Notes in Computer Science

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In the problems of image retrieval and annotation, complete textual tag lists of images play critical roles. However, in real-world applications, the image tags are usually incomplete, thus it is important to learn the complete tags for images. In this paper, we study the problem of image tag complete and proposed a novel method for this problem based on a popular image representation method, convolutional neural network (CNN). The method estimates the complete tags from the convolutional filtering outputs of images based on a linear predictor. The CNN parameters, linear predictor, and the complete tags are learned jointly by our method. We build a minimization problem to encourage the consistency between the complete tags and the available incomplete tags, reduce the estimation error, and reduce the model complexity. An iterative algorithm is developed to solve the minimization problem. Experiments over benchmark image data sets show its effectiveness.

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“…In recent years, Pulse coupled neural network of cat visual cortex model based on Eckhorn [7][8][9] (Pulse Coupled Neural Net, PCNN) has been widely used to study the field of image processing, and shows its superiority, especially in the application of image segmentation, PCNN can effectively Overlap between the separation of the target and the background, It can also deal with the problem of small gray level change and spatial incoherence in the target. The existing algorithms based on PCNN are mostly applied to the segmentation of gray images, until a color image segmentation method based on PCNN is proposed.…”

Section: Introductionmentioning

confidence: 99%

Color Image Segmentation Based on PCNN

Li¹,

Zhang²,

Zhu³

2018

JMI

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In view of the ignition characteristics of PCNN, when the traditional PCNN model is used in image segmentation, the final segmentation images are two valued images. Although the two value image has the advantages of easy identification and convenient storage, but for the following processing of image recognition, image compression and feature extraction, the singleness of two valued image can not meet the needs of people. Therefore, based on the previous work, the maximum Shannon entropy, maximum gray entropy and maximum variance ratio are used to improve the traditional PCNN. The final output of the model has three patterns: color, gray and two values, which is convenient for the subsequent processing of images. Experimental results show that the proposed algorithm can achieve the effective segmentation of color images, and the segmentation effect is significantly better than the traditional algorithm.

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Nuclear norm regularized convolutional Max Pos@Top machine

Cited by 23 publications

References 40 publications

Cross-model convolutional neural network for multiple modality data representation

Cross-model convolutional neural network for multiple modality data representation

A Novel Image Tag Completion Method Based on Convolutional Neural Transformation

Color Image Segmentation Based on PCNN

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