Name
Abstract | ||
|---|---|---|
This paper presents a conceptually simple, and robust, yet highly effective, approach to both texture classification and material categorization. The proposed system is composed of three components: 1) local, highly discriminative, and robust features based on sorted random projections (RPs), built on the universal and information-preserving properties of RPs; 2) an effective bag-of-words global model; and 3) a novel approach for combining multiple features in a support vector machine classifier. The proposed approach encompasses the simplicity, broad applicability, and efficiency of the three methods. We have tested the proposed approach on eight popular texture databases, including Flickr Materials Database, a highly challenging materials database. We compare our method with 13 recent state-of-the-art methods, and the experimental results show that our texture classification system yields the best classification rates of which we are aware of 99.37% for Columbia-Utrecht, 97.16% for Brodatz, 99.30% for University of Maryland Database, and 99.29% for Kungliga Tekniska högskolan-textures under varying illumination, pose, and scale. Moreover, the proposed approach significantly outperforms the current state-of-the-art approach in materials categorization, with an improvement to classification accuracy of 67%. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TCSVT.2014.2359098 | IEEE Trans. Circuits Syst. Video Techn. |
Keywords | Field | DocType |
texture classification system,support vector machines (svms),image fusion,materials textures,kernel methods,information preserving properties,random projection (rp),material classification,flickr materials database,bag-of-words global model,texture classification,fusing sorted random projections,sorted random projections,rotation invariance,texture databases,support vector machine classifier,data fusion,robust texture,material categorization,image classification,image texture,support vector machines,databases,histograms,feature extraction,vectors,kernel,materials | Kernel (linear algebra),Histogram,Computer vision,Pattern recognition,Image texture,Computer science,Support vector machine,Feature extraction,Artificial intelligence,Contextual image classification,Kernel method,Discriminative model | Journal |
Volume | Issue | ISSN |
25 | 3 | 1051-8215 |
Citations | PageRank | References |
9 | 0.43 | 38 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Liu | 1 | 9 | 0.43 |
| Paul W. Fieguth | 2 | 612 | 54.17 |
| Dewen Hu | 3 | 1290 | 101.20 |
| Yingmei Wei | 4 | 17 | 3.52 |
| Gangyao Kuang | 5 | 347 | 31.11 |