Ke Zhang, Fan Zhang, Jia Lu, Yinghua Lu, Jun Kong, Ming Zhang
Journal of Electronic Imaging, Vol. 25, Issue 02, 023030, (April 2016) https://doi.org/10.1117/1.JEI.25.2.023030
TOPICS: Image retrieval, Visualization, Feature extraction, Quantization, Content based image retrieval, Image analysis, Image processing, Information visualization, Lutetium, Binary data
Image description and annotation is an active research topic in content-based image retrieval. How to utilize human visual perception is a key approach to intelligent image feature extraction and representation. This paper has proposed an image feature descriptor called the local structure co-occurrence pattern (LSCP). LSCP extracts the whole visual perception for an image by building a local binary structure, and it is represented by a color-shape co-occurrence matrix which explores the relationship of multivisual feature spaces according to visual attention mechanism. As a result, LSCP not only describes low-level visual features integrated with texture feature, color feature, and shape feature but also bridges high-level semantic comprehension. Extensive experimental results on an image retrieval task on the benchmark datasets, corel-10,000, MIT VisTex, and INRIA Holidays, have demonstrated the usefulness, effectiveness, and robustness of the proposed LSCP.