Name
Abstract | ||
|---|---|---|
A video scene can be defined as a fixed subdivision of a video, or a group of video frames having the same semantic contents. This paper presents a method to perform scene classification under unsupervised clustering environment. A holistic representation of the Spatial Envelope has been proposed to model the scene. One drawback of Spatial Envelope features is that it uses R, G, and B channels separately to extract features for processing. However, individual R, G, and B channels cannot describe color visual information of the image accurately. In this paper, a novel different color channel generated with Fibonacci lattice color quantization indexes is applied to generate Spatial Envelope features to address this drawback. An unsupervised clustering method named as Hyperclique Pattern-KMEANS (HP-KMEANS) is proposed to automatically select constraints for image clustering. Evaluation of the proposed feature extraction algorithm shows promising results for natural scene classification. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3194452.3194476 | PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON COMPUTING AND ARTIFICIAL INTELLIGENCE (ICCAI 2018) |
Keywords | Field | DocType |
classification, color quantization, clustering | Feature extraction algorithm,Pattern recognition,Lattice (order),Computer science,Communication channel,Subdivision,Artificial intelligence,Cluster analysis,Color quantization,Channel (digital image),Fibonacci number | Conference |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haifeng Wang | 1 | 21 | 5.87 |
| Xiaoyan Wang | 2 | 20 | 8.83 |
| Yuchou Chang | 3 | 194 | 15.86 |