Search Results for author: Alessandro Perina

Found 13 papers, 1 papers with code

Latent Constrained Correlation Filter

no code implementations11 Nov 2017 Baochang Zhang, Shangzhen Luan, Chen Chen, Jungong Han, Wei Wang, Alessandro Perina, Ling Shao

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.

Object Recognition Object Tracking

Summarization and Classification of Wearable Camera Streams by Learning the Distributions Over Deep Features of Out-Of-Sample Image Sequences

no code implementations ICCV 2017 Alessandro Perina, Sadegh Mohammadi, Nebojsa Jojic, Vittorio Murino

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.

General Classification

Latent Constrained Correlation Filters for Object Localization

no code implementations7 Jun 2016 Shangzhen Luan, Baochang Zhang, Jungong Han, Chen Chen, Ling Shao, Alessandro Perina, Linlin Shen

There is a neglected fact in the traditional machine learning methods that the data sampling can actually lead to the solution sampling.

Object Localization

Hierarchical learning of grids of microtopics

no code implementations12 Mar 2015 Nebojsa Jojic, Alessandro Perina, Dongwoo Kim

The counting grid is a grid of microtopics, sparse word/feature distributions.

General Classification

3D Pose from Detections

no code implementations17 Feb 2015 Cosimo Rubino, Marco Crocco, Alessandro Perina, Vittorio Murino, Alessio Del Bue

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

no code implementations23 Oct 2014 Alessandro Perina, Nebojsa Jojic

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.

Scene Recognition

Documents as multiple overlapping windows into grids of counts

no code implementations NeurIPS 2013 Alessandro Perina, Nebojsa Jojic, Manuele Bicego, Andrzej Truski

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.

Document Classification Topic Models

Capturing Layers in Image Collections with Componential Models: From the Layered Epitome to the Componential Counting Grid

no code implementations CVPR 2013 Alessandro Perina, Nebojsa Jojic

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

no code implementations26 Apr 2013 Alessandro Perina, Nebojsa Jojic

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

no code implementations NeurIPS 2010 Nebojsa Jojic, Alessandro Perina, Vittorio Murino

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

no code implementations NeurIPS 2009 Alessandro Perina, Marco Cristani, Umberto Castellani, Vittorio Murino, Nebojsa Jojic

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.

General Classification

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