A study on the Image Retrieval Technology Based on Color Feature Extraction
DOI: 10.23977/acss.2018.21003 | Downloads: 15 | Views: 781
Wenlie Zhu 1, Jing Chang 1, Zilan Hu 1
1 South China Business College Guangdong University of Foreign Studies, Guangzhou 510545,China
Corresponding AuthorJing Chang
The text-based image retrieval technology is sufficiently mature now, but it still fails to be accurate. It is urgent to further investigate into the content-based image retrieval technology which is quite new and widely applied to a variety of fields. As color is one of the fundamental features of image, the retrieval based on the color features of image can effectively improve the efficient. In this paper, we analyzed and studied the color-based image retrieval and verified the universality of CBIR system in application with nighttime license plate identification case. To sum up, CBIR has a promising future in application. With the future development, it is believed to have higher retrieval efficiency and similarity when meeting the demand of people for image retrieval so that the users can rapidly and accurately locate the image resources they want against a sea of information and better help can be provided for the image classification.
KEYWORDSImage retrieval, Color features, Color histogram, HSV color space
CITE THIS PAPER
Wenlie, Z., Jing, C., Zilan, H., A study on the Image Retrieval Technology Based on Color Feature Extraction, Advances in Computer, Signals and Systems (2018) 2: 11-18.
 Jitesh Pradhan,Sumit Kumar,Arup Kumar Pal,Haider Banka. A hierarchical CBIR framework using adaptive tetrolet transform and novel histograms from color and shape features[J]. Digital Signal Processing,2018.
 Wajdi Besbas. Content Based Image Retrieval(CBIR) of Face Sketch Images Using WHT Transform Domain[A]. CBEES.Proceedings of 2014 3rd International Conference on Informatics,Environment,Energy and Applications[C].CBEES:,2014:5.
 Nishant Shrivastava,Vipin Tyagi. An efficient technique for retrieval of color images in large databases[J]. Computers and Electrical Engineering,2015,46.
 Xiang-Yang Wang,Hong-Ying Yang,Dong-Ming Li. A new content-based image retrieval technique using color and texture information[J]. Computers and Electrical Engineering,2013,39(3).
 Khadem B, Farahzadeh E, Rajan D, et al. Embedding Visual Words into Concept Space for Action and Scene Recognition[J]. 2010.
 Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15(1):1929-1958.
 Nair V, Hinton G E. Rectified linear units improve restricted boltzmann machines[C]// International Conference on International Conference on Machine Learning. Omnipress, 2010:807-814.
 Zaharia M, Das T, Li H, et al. Discretized streams: fault-tolerant streaming computation at scale[J]. In Proceedings of the 24th ACM Symposium on Operating Systems Principles (SOSP, 2013:423-438.
 Raju U S N, George S, Praneeth V S, et al. Content Based Image Retrieval on Hadoop Framework[C]// IEEE International Congress on Big Data. IEEE, 2015:661-664.