Removal and contraction are basic operations for several methods conceived in order to handle irregular image pyramids, for multi-level image analysis for instance. We give the definitions of removal and contraction operations in the generalized maps framework. We propose a first experimentation of irregular pyramid as a basis for a discrete geometrical modeler that can handle both discrete and...

An irregular pyramid consists of a stack of successively reduced graphs. Each smaller graph is deduced from the preceding one using contraction or removal kernels. A contraction (resp. removal) kernel defines a forest of the initial (resp. dual ) graph, each tree of this forest being reduced to a single vertex (resp. dual vertex) in the reduced graph. A combinatorial map encodes a planar graph ...

Given a CAD model of an object, we would like to automatically generate a vision model and matching procedure that can be used in robot guidance and inspection tasks. We are building a system that can predict features that will appear in a 2D view of a 3D object, represent each such view with a hierarchical, relational structure, group together similar views into view classes, and match an unkn...

In order to achieve good performance in image annotation tasks, it is necessary to combine information from various image features. In our submission, we applied the nonsparse multiple kernel learning for feature combination proposed by Kloft et al.(2009) to the ImageCLEF2009 photo annotation data. Since some of the concepts of the ImageCLEF task are rather abstract, we conjectured that color h...

Spatial Pyramid Matching (SPM) has been a major breakthrough in the field of object and scene recognition. Using this approach, an image is divided into 2 × 2 disjoint sub-windows for each pyramid level l. However, the disjoint arrangement of the sub-windows can be too restrictive, especially when we consider that each window may benefit from broader context. Inspired by human habit in viewing ...

The bag-of-visual-words (BOVW) approaches are widely used in human action recognition. Usually, large vocabulary size of the BOVW is more discriminative for inter-class action classification while small one is more robust to noise and thus tolerant to the intra-class invariance. In this pape, we propose a pyramid vocabulary tree to model local spatio-temporal features, which can characterize th...

We address the problem of generating video features for action recognition. The spatial pyramid and its variants have been very popular feature models due to their success in balancing spatial location encoding and spatial invariance. Although it seems straightforward to extend spatial pyramid to the temporal domain (spatio-temporal pyramid), the large spatio-temporal diversity of unconstrained...

Recently, Bag of Visual Words (BoW) model has shown its success in image classification and retrieval. The key idea behind the BoW model is to quantize the continuous highdimensional space of image features (e.g. SIFT [1]) to a manageable visual codebook. The quality of the visual codebook has an important impact on BoW-based methods. Different from the existing techniques, such as Kernel codeb...

In the past decades, many different techniques have been used to improve face recognition performance. The most common and well-studied ways are to use the whole face image to build a subspace based on the reduction of dimensionality. Differing from methods above, we consider face recognition as an image classification problem. The face images of the same person are considered to fall into the ...