Recent Studies Around the Neocognitron
Neural Information Processing
SVM-based segmentation and classification of remotely sensed data
International Journal of Remote Sensing
Boosting Shift-Invariant Features
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Evaluation of pooling operations in convolutional architectures for object recognition
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Research frontier: deep machine learning--a new frontier in artificial intelligence research
IEEE Computational Intelligence Magazine
From engineering diagrams to engineering models: Visual recognition and applications
Computer-Aided Design
Semantics extraction from images
Knowledge-driven multimedia information extraction and ontology evolution
An AER spike-processing filter simulator and automatic VHDL generator based on cellular automata
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
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The detection and recognition of generic object categories with invariance to viewpoint, illumination, and clutter requires the combination of a feature extractor and a classifier. We show that architectures such as convolutional networks are good at learning invariant features, but not always optimal for classification, while Support Vector Machines are good at producing decision surfaces from wellbehaved feature vectors, but cannot learn complicated invariances. We present a hybrid system where a convolutional network is trained to detect and recognize generic objects, and a Gaussian-kernel SVM is trained from the features learned by the convolutional network. Results are given on a large generic object recognition task with six categories (human figures, four-legged animals, airplanes, trucks, cars, and "none of the above"), with multiple instances of each object category under various poses, illuminations, and backgrounds. On the test set, which contains different object instances than the training set, an SVM alone yields a 43.3% error rate, a convolutional net alone yields 7.2% and an SVM on top of features produced by the convolutional net yields 5.9%.