Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Pictorial Structures for Object Recognition
International Journal of Computer Vision
A Robust Framework For Eigenspace Image Reconstruction
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Generic Model Abstraction from Examples
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling Scenes with Local Descriptors and Latent Aspects
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Object Recognition as Many-to-Many Feature Matching
International Journal of Computer Vision
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region-Based Hierarchical Image Matching
International Journal of Computer Vision
Kernel Codebooks for Scene Categorization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Entropy controlled Laplacian regularization for least square regression
Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Max-Min Distance Analysis by Using Sequential SDP Relaxation for Dimension Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Manifold elastic net: a unified framework for sparse dimension reduction
Data Mining and Knowledge Discovery
Signal Processing
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Object classification is an important issue in multimedia information retrieval. Usually, we can use images from multiple views (or multi-view images) to describe an object for classification. However, two issues remain unsolved. First, exploiting the spatial relations of local features from different view images for object classification. Second, accelerating the multi-view object classification process. To solve these two problems, we propose fast multi-view segment graph kernel (FMSGK). Given a set of multi-view images for an object, we segment each of them in terms of its color intensity distribution. And inter- and intra-view segment graphs are constructed to describe the spatial relations of the segments between and within view images respectively. Then, these two types of graphs are integrated into a so-called multi-view segment graph. And the kernel between objects is computed by accumulating all matchings' of walk structures between their corresponding multi-view segment graphs. Since computing the kernel directly is highly time-consuming, an accelerating algorithm is derived. Finally, a multi-class support vector machine (SVM) (Duda et al., 2000 [19]; Wang et al., 2008 [32]; Dai and Mai, 2012 [6]) is trained based on the computed kernels for object classification. The experimental results on three data sets validate the effectiveness of our approach.