Adaptively Combining Local with Global Information for Natural Scenes Categorization
スポンサーリンク
概要
- 論文の詳細を見る
This paper proposes the Extended Bag-of-Visterms (EBOV) to represent semantic scenes. In previous methods, most representations are bag-of-visterms (BOV), where visterms referred to the quantized local texture information. Our new representation is built by introducing global texture information to extend standard bag-of-visterms. In particular we apply the adaptive weight to fuse the local and global information together in order to provide a better visterm representation. Given these representations, scene classification can be performed by pLSA (probabilistic Latent Semantic Analysis) model. The experiment results show that the appropriate use of global information improves the performance of scene classification, as compared with BOV representation that only takes the local information into account.
- 2008-07-01
著者
-
Yang Xu
Beijing Jiaotong Univ. Beijing Chn
-
LIU Shuoyan
Institute of Computer Science and Engineering, Beijing Jiaotong University
-
XU De
Institute of Computer Science and Engineering, Beijing Jiaotong University
-
YANG Xu
Institute of Computer Science and Engineering, Beijing Jiaotong University
-
Xu De
Beijing Jiaotong Univ. Beijing Chn
-
Xu De
Institute Of Computer Science And Engineering Beijing Jiaotong University
-
Xu De
Institute Of Computer & Engineering Beijing Jiaotong University
-
Yang Xu
Institute Of Computer Science And Engineering Beijing Jiaotong University
-
Liu Shuoyan
Institute Of Computer Science And Engineering Beijing Jiaotong University
-
Liu Shuoyan
Institute Of Computer And Engineering Beijing Jiaotong University
-
Liu Shouyan
Institute Of Computer Science And Engineering Beijing Jiaotong University
-
Liu Shuoyan
Institute Of Computer & Engineering Beijing Jiaotong University
関連論文
- Natural Scene Classification Based on Integrated Topic Simplex
- Adaptively Combining Local with Global Information for Natural Scenes Categorization
- Multi-Scale Multi-Level Generative Model in Scene Classification
- Color Constancy Based on Image Similarity
- How the Number of Interest Points Affect Scene Classification
- Category Constrained Learning Model for Scene Classification
- Natural Scene Classification Based on Integrated Topic Simplex
- Edge-Based Color Constancy via Support Vector Regression
- Combining Attention Model with Hierarchical Graph Representation for Region-Based Image Retrieval
- A Multi-Scale Adaptive Grey World Algorithm(Image Recognition, Computer Vision)
- A Novel Tone Mapping Based on Double-Anchoring Theory for Displaying HDR Images
- Adaptively Combining Local with Global Information for Natural Scenes Categorization
- Action Recognition Using Visual-Neuron Feature
- 2D Log-Gabor Wavelet Based Action Recognition
- Adaptive Non-linear Intensity Mapping Based Salient Region Extraction
- Modeling Bottom-Up Visual Attention for Color Images
- A Visual Inpainting Method Based on the Compressed Domain(Image Processing and Video Processing)
- Moving Object Completion on the Compressed Domain
- Color Constancy Based on Effective Regions
- Predicting DataSpace Retrieval Using Probabilistic Hidden Information
- Optimal Gaussian Kernel Parameter Selection for SVM Classifier
- Multi-Scale Multi-Level Generation Model in Scene Classification
- Discriminating Semantic Visual Words for Scene Classification
- A Novel Saliency-Based Graph Learning Framework with Application to CBIR
- Scene Categorization with Classified Codebook Model
- Kernel Optimization Based Semi-Supervised KBDA Scheme for Image Retrieval
- Global-Context Based Salient Region Detection in Nature Images