Generic Visual Object Categorization (VOC) aims at predicting whether at least one or several objects of some given categories are present in an image. In fact, VOC is a fundamental problem in computer vision and pattern recognition, and has become an important research topic due to the wide range of possible applications such as video monitoring, video coding systems, security access control, ...

This thesis is focused on recognising emotions of different subjects through facial expressions in 2D images. We will go through the multiple stages of this problem where we aim to take maximum advantage of supervised algorithms and labelled information. We will compare different pixel processing techniques and show that the histogram based ones, like HOG and LBP, have the best performance for ...

Multiset canonical correlation analysis is a powerful technique for analyzing linear correlations among multiple representation data. However, it usually fails to discover the intrinsic sparse reconstructive relationship and discriminating structure of multiple data spaces in real-world applications. In this paper, by taking discriminative information of within-class and between-class sparse re...

Sparse coding can learn good robust representation to noise and model more higher-order representation for image classification. However, the inference algorithm is computationally expensive even though the supervised signals are used to learn compact and discriminative dictionaries in sparse coding techniques. Luckily, a simplified neural network module (SNNM) has been proposed to directly lea...

Spoken language recognition requires a series of signal processing steps and learning algorithms to model distinguishing characteristics of different languages. In this paper, we present a sparse discriminative feature learning framework for language recognition. We use sparse coding, an unsupervised method, to compute efficient representations for spectral features from a speech utterance whil...

We propose a discriminative patch-level spatial layout model suitable for training with weak supervision. We start from a block-sparse model of patch appearance based on the normalized Fisher vector representation. The appearance model is responsible for i) selecting a discriminative subset of visual words, and ii) identifying distinctive patches assigned to the selected subset. These patches a...

In a sparse representation based recognition scheme, it is critical to learn a desired dictionary, aiming both good representational power and discriminative performance. In this paper, we propose a new dictionary learning model for recognition applications, in which three strategies are adopted to achieve these two objectives simultaneously. First, a block-diagonal constraint is introduced int...