Search Results for author:
Found 13 papers, 0 papers with code
Latent Constrained Correlation Filter
In this paper, we introduce an intermediate step -- solution sampling -- after the data sampling step to form a subspace, in which an optimal solution can be estimated.
Summarization and Classification of Wearable Camera Streams by Learning the Distributions Over Deep Features of Out-Of-Sample Image Sequences
In particular, we use constrained Markov walks over a counting grid for modeling image sequences, which not only yield good latent representations, but allow for excellent classification with only a handful of labeled training examples of the new scenes or objects, a scenario typical in lifelogging applications.
Output Constraint Transfer for Kernelized Correlation Filter in Tracking
Kernelized Correlation Filter (KCF) is one of the state-of-the-art object trackers.
Latent Constrained Correlation Filters for Object Localization
There is a neglected fact in the traditional machine learning methods that the data sampling can actually lead to the solution sampling.
Sparse Representation Classification With Manifold Constraints Transfer
The fact that image data samples lie on a manifold has been successfully exploited in many learning and inference problems.
Hierarchical learning of grids of microtopics
The counting grid is a grid of microtopics, sparse word/feature distributions.
3D Pose from Detections
We present a novel method to infer, in closed-form, a general 3D spatial occupancy and orientation of a collection of rigid objects given 2D image detections from a sequence of images.
Capturing spatial interdependence in image features: the counting grid, an epitomic representation for bags of features
The space of all possible feature count combinations is constrained both by the properties of the larger scene and the size and the location of the window into it.
Documents as multiple overlapping windows into grids of counts
The counting grid \cite{cgUai} models this spatial metaphor literally: it is multidimensional grid of word distributions learned in such a way that a document's own distribution of features can be modeled as the sum of the histograms found in a window into the grid.
Capturing Layers in Image Collections with Componential Models: From the Layered Epitome to the Componential Counting Grid
Recently, the Counting Grid (CG) model [5] was developed to represent each input image as a point in a large grid of feature counts.
In the sight of my wearable camera: Classifying my visual experience
We introduce and we analyze a new dataset which resembles the input to biological vision systems much more than most previously published ones.
Structural epitome: a way to summarize one’s visual experience
In order to study the properties of total visual input in humans, a single subject wore a camera for two weeks capturing, on average, an image every 20 seconds (www. research. microsoft. com/~jojic/aihs).
Free energy score space
Score functions induced by generative models extract fixed-dimension feature vectors from different-length data observations by subsuming the process of data generation, projecting them in highly informative spaces called score spaces.
