Cross Domains Image Processing via Fisher Linear Discriminative Analysis and Bregman Divergence

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Zandifar M.; Tahmoresnezhad J.

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Journal Paper

Paper Information

Journal: TABRIZ JOURNAL OF ELECTRICAL ENGINEERING Year:2019 | Volume:49 | Issue:4 (90) Page(s): 1681-1696

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Information Journal Paper

Title

Cross Domains Image Processing via Fisher Linear Discriminative Analysis and Bregman Divergence

Author(s)

Zandifar M. | Tahmoresnezhad J. | Issue Writer Certificate

Keywords

Image processingQ2

transfer learningQ1

bregman divergenceQ1

conditional distribution difference reductionQ1

dimensionality reduction.Q2

Abstract

Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. The conventional Image processing algorithms cannot perform well in scenarios where the training images (source domain) that are used to learn the model have a different distribution with test images (target domain). In fact, the existence of conditional distribution difference across the source and target domains degrades the performance of model. Domain adaptation and transfer learning are promising solutions that aim to generalize a learning model across training and test data with different distributions. In this paper, we address the problem of unsupervised cross domain Image processing in which no labels are available in test images. In fact, the proposed method transfers the source and target domains into a shared low dimensional FLDA-based subspace in an unsupervised manner. Our proposed method minimizes the conditional probability distribution difference of the source and target data via bregman divergence. We provide a projection matrix to map the source and target data into a common subspace on which the between class scatter matrix is maximized and within class scatter matrix and cross domain distributions are minimized. Extensive experiments on 58 cross-domain image classification tasks over six public datasets reveal that our proposed method outperforms the state-of-the-art cross domain Image processing approaches.

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APA: Copy

Zandifar, M., & Tahmoresnezhad, J.. (2019). Cross Domains Image Processing via Fisher Linear Discriminative Analysis and Bregman Divergence. TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, 49(4 (90) ), 1681-1696. SID. https://sid.ir/paper/382288/en

Vancouver: Copy

Zandifar M., Tahmoresnezhad J.. Cross Domains Image Processing via Fisher Linear Discriminative Analysis and Bregman Divergence. TABRIZ JOURNAL OF ELECTRICAL ENGINEERING[Internet]. 2019;49(4 (90) ):1681-1696. Available from: https://sid.ir/paper/382288/en

IEEE: Copy

M. Zandifar, and J. Tahmoresnezhad, “Cross Domains Image Processing via Fisher Linear Discriminative Analysis and Bregman Divergence,” TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, vol. 49, no. 4 (90) , pp. 1681–1696, 2019, [Online]. Available: https://sid.ir/paper/382288/en

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