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Color Adjustment Based on Support Vector Regression for Multi-View Video
Huadong Sun,Xuesong Jin,Zhipeng Fan,Lizhi Zhang,Qian Wu 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.1
Significant color discrepancies between the different camera views can be observed in multi-view video sequences. In this paper, a color adjustment algorithm based on support vector regression is proposed. A mapping function is established by extracted feature points from original-image and target-image. Then the mapping function is applied to the original image to obtain the corrected image. Experimental results show that the proposed method can produce good correction result. It also shows that the color differences between multi-view video can be effectively reduced by SVR.
Content Based Image Retrieval Scheme using Color, Texture and Shape Features
Zhijie Zhao,Qin Tian,Huadong Sun,Xuesong Jin,Junxi Guo 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.1
A novel approach of Content Based Image Retrieval(CBIR), which combines color, texture and shape descriptors to represent the features of the image, is discussed in this paper. The proposed scheme is based on three noticeable algorithms: color distribution entropy(CDE), color level co-occurrence(CLCM) and invariant moments. CDE takes the correlation of the color spatial distribution in an image into consideration. CLCM matrix is the texture feature of the image, which is a new proposed descriptor that is grounded on co-occurrence matrix to seize the alteration of the texture. Hu invariant moments are frequently used owing to its invariance under translation, changes in scale, and also rotation. The proposed scheme achieves a modest retrieval result by utilizing these diverse and primitive image descriptors, at the same time, the retrieval result is better when use the texture feature alone which we proposed than use gray level co-occurrence. The similarity measure matrix is based upon Euclidean distance.