Nested sparse quantization for efficient feature coding
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Nobody likes Mondays: foreground detection and behavioral patterns analysis in complex urban scenes
Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream
Toward a practical visual object recognition system
Proceedings of the Fourth Symposium on Information and Communication Technology
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We present a hierarchical model that learns image decompositions via alternating layers of convolutional sparse coding and max pooling. When trained on natural images, the layers of our model capture image information in a variety of forms: low-level edges, mid-level edge junctions, high-level object parts and complete objects. To build our model we rely on a novel inference scheme that ensures each layer reconstructs the input, rather than just the output of the layer directly beneath, as is common with existing hierarchical approaches. This makes it possible to learn multiple layers of representation and we show models with 4 layers, trained on images from the Caltech-101 and 256 datasets. When combined with a standard classifier, features extracted from these models outperform SIFT, as well as representations from other feature learning methods.