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Found 8 papers, 0 papers with code
Supervised Kernel Descriptors for Visual Recognition
In visual recognition tasks, the design of low level image feature representation is fundamental.
Scalable $k$-NN graph construction
The $k$-NN graph has played a central role in increasingly popular data-driven techniques for various learning and vision tasks; yet, finding an efficient and effective way to construct $k$-NN graphs remains a challenge, especially for large-scale high-dimensional data.
Hybrid Affinity Propagation
We formulate this problem as finding a few image exemplars to represent the image set semantically and visually, and solve it in a hybrid way by exploiting both visual and textual information associated with images.
Fast Neighborhood Graph Search using Cartesian Concatenation
This structure augments the neighborhood graph with a bridge graph.
Fast Approximate $K$-Means via Cluster Closures
Traditional $k$-means is an iterative algorithm---in each iteration new cluster centers are computed and each data point is re-assigned to its nearest center.
Optimized Cartesian $K$-Means
In OCKM, multiple sub codewords are used to encode the subvector of a data point in a subspace.
Group $K$-Means
We study how to learn multiple dictionaries from a dataset, and approximate any data point by the sum of the codewords each chosen from the corresponding dictionary.
Content adaptive screen image scaling
This paper proposes an efficient content adaptive screen image scaling scheme for the real-time screen applications like remote desktop and screen sharing.
