Kernel methods are a class of well established and successful algorithms for pattern
analysis thanks to their mathematical elegance and good performance. Numerous nonlinear …

Distance metric is a key issue in many machine learning algorithms. This paper considers a
general problem of learning from pairwise constraints in the form of must-links and cannot …

Pictorial structure (PS) models are extensively used for part-based recognition of scenes,
people, animals and multi-part objects. To achieve tractability, the structure and …

In object recognition in general and in face detection in particular, data alignment is
necessary to achieve good classification results with certain statistical learning approaches …

Precision and recall are classical measures used in machine learning. However, they are
based on exact matching. This results in binary classification where the predicted item is …

Fisher kernels provide a commonly used vectorial representation of structured objects. The
paper presents a technique that exploits label information to improve the object …

Recognizing humans, estimating their pose and segmenting their body parts are key to high-
level image understanding. Because humans are highly articulated, the range of …

We formulate a deformable template model for objects with an efficient mechanism for
computation and parameter estimation. The data consists of binary oriented edge features …

This paper considers the person verification problem in modern surveillance and video
retrieval systems. The problem is to identify whether a pair of face or human body images is …

Person-job fit is to match candidates and job posts on online recruitment platforms using
machine learning algorithms. The effectiveness of matching algorithms heavily depends on …