There are two main strategies to tackle scene classification: holistic and semantic. The former characterizes a scene using its global features, while the latter represents a scene by modeling its internal object configuration. Holistic strategy is good at representing scenes with simple contents, but it does not represent well complex scenes that consist of multiple objects. By contrast, semantic strategy is advantageous at recognizing scenes with complex objects, but it does not work well for simple scenes. In this paper, we propose to integrate holistic and semantic strategies to cope with scene classification. In particular, we exploit a deep learning algorithm to learn features for scene representation in the holistic way. For the semantic strategy, we explore a semantic spatial pyramid to represent the spatial object configuration of scenes. The holistic and semantic strategies are integrated using a method proposed by us. Experimental results on a benchmark natural scene dataset demonstrate the effectiveness of our proposed hybrid approach for scene classification, by comparing to several state-of-the-art algorithms. Copyright © 2014 IEEE.
|Title of host publication||2014 22nd International Conference on Pattern Recognition (ICPR 2014)|
|Place of Publication||Piscataway, NJ|
|Publication status||Published - 2014|
CitationChen, Z., Chi, Z., & Fu, H. (2014). A hybrid holistic/semantic approach for scene classification. In 2014 22nd International Conference on Pattern Recognition (ICPR 2014) (pp. 2299-2304). Piscataway, NJ: IEEE.
- Scene classification
- Holistic representation
- Semantic representation
- Semantic spatial pyramid