Concept

Annotation automatique d'images

Publications associées (75)

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Sparse Binary Features for Image Classification

Johann Paratte

In this work a new method for automatic image classification is proposed. It relies on a compact representation of images using sets of sparse binary features. This work first evaluates the Fast Retina Keypoint binary descriptor and proposes imp ...

2013

Our research addresses the need for an efficient, effective, and interactive access to large-scale image collections. Image retrieval needs are evolving beyond the capabilities of the traditional indexing based on manual annotation, and the most desirable ...

EPFL2013

Transformation-Invariant Analysis of Visual Signals with Parametric Models

Elif Vural

The analysis of collections of visual data, e.g., their classification, modeling and clustering, has become a problem of high importance in a variety of applications. Meanwhile, image data captured in uncontrolled environments by arbitrary users is very li ...

EPFL2013

Separable Filter Learning with Tensor Decomposition

Bugra Tekin

Learned image features can provide great accuracy in many Computer Vision tasks. However, when the convolution filters used to learn image features are numerous and not separable, feature extraction becomes computationally de- manding and impractical to us ...

2013

Learning Separable Filters with Shared Parts

Bugra Tekin

2013

Grassmannian Regularized Structured Multi-View Embedding for Image Classification

Xinchao Wang

Images are usually represented by features from multiple views, e.g., color and texture. In image classification, the goal is to fuse all the multi-view features in a reasonable manner and achieve satisfactory classification performance. However, the featu ...

Institute of Electrical and Electronics Engineers2013

Learning Separable Filters

Pascal Fua, Vincent Lepetit, Roberto Rigamonti

While learned image features can achieve great accuracy on different Computer Vision problems, their use in real-world situations is still very limited as their extraction is typically time-consuming. We therefore propose a method to learn image features t ...

2012

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In this paper we analyze empirically the importance of sparsifying representations for classification purposes. We focus on those obtained by convolving images with linear filters, which can be either hand designed or learned, and perform extensive experim ...

2012

In this report we study the ways to exploit the vast amount of information inherent in the plenoptic space and constraints of the plenoptic function to improve the efficiency of image retrieval, recognition and matching techniques. The specific application ...

2012

Discovering relevant spatial filterbanks for VHR image classification

Devis Tuia

In very high resolution (VHR) image classification it is common to use spatial filters to enhance the discrimination among landuses related to similar spectral properties but different spatial characteristics. However, the filters types that can be used ar ...

2012