Inferring sparse, overcomplete image codes using an efficient coding framework · Contents. NIPS '97: Proceedings of the 1997 conference on Advances in neural ...
This paper applies mathematical analysis to a specific formalization of SCA using synthetic image models, hoping to gain insight into what might emerge from ...
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What is sparse coding in image processing?
Abstract. Images can be coded accurately using a sparse set of vectors from a learned overcomplete dictionary, with potential applications in image ...
Missing: Inferring | Show results with:Inferring
The sparse coding model works by finding a dictionary of basis functions such that only a few are needed to reconstruct any given image. Sparse coding was ...
We conclude that sparse coding can generate a suitable general representation for subsequent inference tasks.
This work describes a conceptually simple method for structured sparse coding and dictionary design. Supposing a dictionary with K atoms, we introduce a.
These sparse coding algorithms work directly on the objects, and are thus constrained to modeling only simple signals, e.g., aligned faces and digits. For ...
The aim of this study is to evaluate how the optimal representation changes when the system is constrained by limited capacity units.
Missing: Inferring | Show results with:Inferring
This paper leverages a self-explanatory re- formulation of sparse representation, i.e., linking the learned dictionary atoms with the original feature spaces ...
The sparse coding model posits that the visual system has evolved to efficiently code natural stimuli using a sparse set of features from an overcomplete ...