Content-based image retrieval model based on cost sensitive learning

Abstract How to retrieve the desired images quickly and accurately from the large scale image database has become a hot topic in the field of multimedia research. Many content-based image retrieval (CBIR) technologies already exist, but they are not always satisfactory. In many applications, the CBIR model based on machine learning relies heavily on the distance metric between samples. Although the traditional distance metric methods are simple and convenient, it is not always appropriate for CBIR tasks. In this paper, a novel distance metric learning (DML) method based on cost sensitive learning (CSL) is studied, and then it is used in a large margin distribution learning machine (LDM) to replace the traditional kernel functions. The improved LDM also takes into account CSL, and which is called CS-DLDM. Finally, CS-DLDM model is applied to CBIR tasks for implementation classification. We compare the proposed CS-DLDM model with other classifiers based on CSL. The experimental results show that the proposed CS-DLDM model not only has satisfactory classification performance but also the lowest misclassification cost, can effectively avoid the class imbalance of sample.

[1]  N. Cristianini,et al.  On Kernel-Target Alignment , 2001, NIPS.

[2]  Yong Luo,et al.  Cost-Sensitive Feature Selection by Optimizing F-Measures , 2018, IEEE Transactions on Image Processing.

[3]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[4]  Ömer Faruk Arar,et al.  Software defect prediction using cost-sensitive neural network , 2015, Appl. Soft Comput..

[5]  Yang Wang,et al.  Cost-sensitive boosting for classification of imbalanced data , 2007, Pattern Recognit..

[6]  Wei Liu,et al.  Learning Distance Metrics with Contextual Constraints for Image Retrieval , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[7]  Cong Jin,et al.  Image distance metric learning based on neighborhood sets for automatic image annotation , 2016, J. Vis. Commun. Image Represent..

[8]  Steven M. Seitz,et al.  Photo tourism: exploring photo collections in 3D , 2006, ACM Trans. Graph..

[9]  José Salvador Sánchez,et al.  On the effectiveness of preprocessing methods when dealing with different levels of class imbalance , 2012, Knowl. Based Syst..

[10]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[11]  Luo Si,et al.  Collaborative image retrieval via regularized metric learning , 2006, Multimedia Systems.

[12]  Alberto Del Bimbo,et al.  Automatic image annotation via label transfer in the semantic space , 2016, Pattern Recognit..

[13]  Giles M. Foody,et al.  Improving specific class mapping from remotely sensed data by cost-sensitive learning , 2017 .

[14]  Yueting Zhuang,et al.  Stable multi-label boosting for image annotation with structural feature selection , 2011, Science China Information Sciences.

[15]  Li Yujian,et al.  A Normalized Levenshtein Distance Metric , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Nello Cristianini,et al.  Learning the Kernel Matrix with Semidefinite Programming , 2002, J. Mach. Learn. Res..

[17]  Kilian Q. Weinberger,et al.  Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.

[18]  Bolei Zhou,et al.  Learning Deep Features for Scene Recognition using Places Database , 2014, NIPS.

[19]  Changyin Sun,et al.  Discriminative Multi-View Interactive Image Re-Ranking , 2017, IEEE Transactions on Image Processing.

[20]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[21]  Cong Jin,et al.  Content-Based Image Retrieval Based on Shape Similarity Calculation , 2017 .

[22]  Bernhard Schölkopf,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.

[23]  Rong Jin,et al.  A Boosting Framework for Visuality-Preserving Distance Metric Learning and Its Application to Medical Image Retrieval , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Changyin Sun,et al.  SERVE: Soft and Equalized Residual VEctors for image retrieval , 2016, Neurocomputing.

[25]  Shasha Wang,et al.  Cost-sensitive Bayesian network classifiers , 2014, Pattern Recognit. Lett..

[26]  Leonidas J. Guibas,et al.  The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.

[27]  Stephen P. Boyd,et al.  Semidefinite Programming , 1996, SIAM Rev..

[28]  Bernhard Schölkopf,et al.  Cost-Sensitive Active Learning With Lookahead: Optimizing Field Surveys for Remote Sensing Data Classification , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Jason J. Corso,et al.  Semi-Supervised Nonlinear Distance Metric Learning via Forests of Max-Margin Cluster Hierarchies , 2014, IEEE Transactions on Knowledge and Data Engineering.

[30]  Liangxiao Jiang,et al.  A Novel Distance Function: frequency difference Metric , 2014, Int. J. Pattern Recognit. Artif. Intell..

[31]  Jing Zhang,et al.  Cost-Sensitive Large margin Distribution Machine for classification of imbalanced data , 2016, Pattern Recognit. Lett..