ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Delving deeper into the whorl of flower segmentation
Image and Vision Computing
CrowdSearch: exploiting crowds for accurate real-time image search on mobile phones
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A food image recognition system with multiple kernel learning
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Interactive learning of heterogeneous visual concepts with local features
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Object classification using heterogeneous co-occurrence features
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Visual recognition with humans in the loop
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Object classification using heterogeneous co-occurrence features
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Medical image classification with multiple kernel learning
ICIMCS '10 Proceedings of the Second International Conference on Internet Multimedia Computing and Service
Variable Sparsity Kernel Learning
The Journal of Machine Learning Research
Whorl identification in flower: a Gabor based approach
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Hierarchical spatial matching kernel for image categorization
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Novel fusion methods for pattern recognition
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Two-stage augmented kernel matrix for object recognition
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Multiple kernel learning via distance metric learning for interactive image retrieval
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Images as sets of locally weighted features
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Non-sparse multiple kernel fisher discriminant analysis
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Modulating Shape Features by Color Attention for Object Recognition
International Journal of Computer Vision
Textural features in flower classification
Mathematical and Computer Modelling: An International Journal
Multi-organ plant identification
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A rapid flower/leaf recognition system
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Effective use of frequent itemset mining for image classification
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TriCoS: a tri-level class-discriminative co-segmentation method for image classification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Efficient optimization for low-rank integrated bilinear classifiers
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Leafsnap: a computer vision system for automatic plant species identification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Reduced analytical dependency modeling for classifier fusion
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
A new biologically inspired color image descriptor
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Pairwise rotation invariant co-occurrence local binary pattern
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Comparison of mid-level feature coding approaches and pooling strategies in visual concept detection
Computer Vision and Image Understanding
Multiple spectral kernel learning and a gaussian complexity computation
Neural Computation
Proceedings of the 21st ACM international conference on Multimedia
Efficient segmentation of leaves in semi-controlled conditions
Machine Vision and Applications
Boosted kernel for image categorization
Multimedia Tools and Applications
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We investigate to what extent combinations of features can improve classification performance on a large dataset of similar classes. To this end we introduce a 103 class flower dataset. We compute four different features for the flowers, each describing different aspects, namely the local shape/texture, the shape of the boundary, the overall spatial distribution of petals, and the colour. We combine the features using a multiple kernel framework with a SVM classifier. The weights for each class are learnt using the method of Varma and Ray [16], which has achieved state of the art performance on other large dataset, such as Caltech 101/256. Our dataset has a similar challenge in the number of classes, but with the added difficulty of large between class similarity and small within class similarity. Results show that learning the optimum kernel combination of multiple features vastly improves the performance, from 55.1% for the best single feature to 72.8% for the combination of all features.