1,329 research outputs found
Contribution to supervised representation learning: algorithms and applications.
278 p.In this thesis, we focus on supervised learning methods for pattern categorization. In this context, itremains a major challenge to establish efficient relationships between the discriminant properties of theextracted features and the inter-class sparsity structure.Our first attempt to address this problem was to develop a method called "Robust Discriminant Analysiswith Feature Selection and Inter-class Sparsity" (RDA_FSIS). This method performs feature selectionand extraction simultaneously. The targeted projection transformation focuses on the most discriminativeoriginal features while guaranteeing that the extracted (or transformed) features belonging to the sameclass share a common sparse structure, which contributes to small intra-class distances.In a further study on this approach, some improvements have been introduced in terms of theoptimization criterion and the applied optimization process. In fact, we proposed an improved version ofthe original RDA_FSIS called "Enhanced Discriminant Analysis with Class Sparsity using GradientMethod" (EDA_CS). The basic improvement is twofold: on the first hand, in the alternatingoptimization, we update the linear transformation and tune it with the gradient descent method, resultingin a more efficient and less complex solution than the closed form adopted in RDA_FSIS.On the other hand, the method could be used as a fine-tuning technique for many feature extractionmethods. The main feature of this approach lies in the fact that it is a gradient descent based refinementapplied to a closed form solution. This makes it suitable for combining several extraction methods andcan thus improve the performance of the classification process.In accordance with the above methods, we proposed a hybrid linear feature extraction scheme called"feature extraction using gradient descent with hybrid initialization" (FE_GD_HI). This method, basedon a unified criterion, was able to take advantage of several powerful linear discriminant methods. Thelinear transformation is computed using a descent gradient method. The strength of this approach is thatit is generic in the sense that it allows fine tuning of the hybrid solution provided by different methods.Finally, we proposed a new efficient ensemble learning approach that aims to estimate an improved datarepresentation. The proposed method is called "ICS Based Ensemble Learning for Image Classification"(EM_ICS). Instead of using multiple classifiers on the transformed features, we aim to estimate multipleextracted feature subsets. These were obtained by multiple learned linear embeddings. Multiple featuresubsets were used to estimate the transformations, which were ranked using multiple feature selectiontechniques. The derived extracted feature subsets were concatenated into a single data representationvector with strong discriminative properties.Experiments conducted on various benchmark datasets ranging from face images, handwritten digitimages, object images to text datasets showed promising results that outperformed the existing state-ofthe-art and competing methods
Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification
Person re-identification (re-id) aims to match pedestrians observed by
disjoint camera views. It attracts increasing attention in computer vision due
to its importance to surveillance system. To combat the major challenge of
cross-view visual variations, deep embedding approaches are proposed by
learning a compact feature space from images such that the Euclidean distances
correspond to their cross-view similarity metric. However, the global Euclidean
distance cannot faithfully characterize the ideal similarity in a complex
visual feature space because features of pedestrian images exhibit unknown
distributions due to large variations in poses, illumination and occlusion.
Moreover, intra-personal training samples within a local range are robust to
guide deep embedding against uncontrolled variations, which however, cannot be
captured by a global Euclidean distance. In this paper, we study the problem of
person re-id by proposing a novel sampling to mine suitable \textit{positives}
(i.e. intra-class) within a local range to improve the deep embedding in the
context of large intra-class variations. Our method is capable of learning a
deep similarity metric adaptive to local sample structure by minimizing each
sample's local distances while propagating through the relationship between
samples to attain the whole intra-class minimization. To this end, a novel
objective function is proposed to jointly optimize similarity metric learning,
local positive mining and robust deep embedding. This yields local
discriminations by selecting local-ranged positive samples, and the learned
features are robust to dramatic intra-class variations. Experiments on
benchmarks show state-of-the-art results achieved by our method.Comment: Published on Pattern Recognitio
Ensemble learning via feature selection and multiple transformed subsets: Application to image classification
[EN]In the machine learning field, especially in classification tasks, the model's design and construction are very important. Constructing the model via a limited set of features may sometimes bound the classification performance and lead to non-optimal performances that some algorithms can provide. To this end, Ensemble learning methods were proposed in the literature. These methods' main goal is to learn a set of models that provide features or predictions whose joint use could lead to a performance better than that obtained by the single model. In this paper, we propose three variants of a new efficient ensemble learning approach that was able to enhance the classification performance of a linear discriminant embedding method. As a case study we consider the efficient "Inter-class sparsity discriminative least square regression" method. We seek the estimation of an enhanced data representation. Instead of deploying multiple classifiers on top of the transformed features, we target the estimation of multiple extracted feature subsets obtained by multiple learned linear embeddings. These are associated with subsets of ranked original features. Multiple feature subsets were used for estimating the transformations. The derived extracted feature subsets were concatenated to form a single data representation vector that is used in the classification process. Many factors were studied and investigated in this paper including (Parameter combinations, number of models, different training percentages, feature selection methods combinations, etc.). Our proposed approach has been benchmarked on different image datasets of various sizes and types (faces, objects and scenes). The proposed scheme achieved competitive performance on four face image datasets (Extended Yale B, LFW-a, Gorgia and FEI) as well as on the COIL20 object dataset and the Outdoor Scene dataset. We measured the performance of our proposed schemes in comparison to (the single model ICS_DLSR, RDA_GD, RSLDA, PCE, LDE, LDA, SVM as well as the KNN algorithm) The conducted experiments showed that the proposed approach can enhance the classification performance in an efficient manner compared to the single-model based learning and was able to outperform its competing methods
REPRESENTATION LEARNING FOR ACTION RECOGNITION
The objective of this research work is to develop discriminative representations for human
actions. The motivation stems from the fact that there are many issues encountered while
capturing actions in videos like intra-action variations (due to actors, viewpoints, and duration),
inter-action similarity, background motion, and occlusion of actors. Hence, obtaining
a representation which can address all the variations in the same action while maintaining
discrimination with other actions is a challenging task. In literature, actions have been represented
either using either low-level or high-level features. Low-level features describe
the motion and appearance in small spatio-temporal volumes extracted from a video. Due
to the limited space-time volume used for extracting low-level features, they are not able
to account for viewpoint and actor variations or variable length actions. On the other hand,
high-level features handle variations in actors, viewpoints, and duration but the resulting
representation is often high-dimensional which introduces the curse of dimensionality. In
this thesis, we propose new representations for describing actions by combining the advantages
of both low-level and high-level features. Specifically, we investigate various linear
and non-linear decomposition techniques to extract meaningful attributes in both high-level
and low-level features. In the first approach, the sparsity of high-level feature descriptors is leveraged to build
action-specific dictionaries. Each dictionary retains only the discriminative information
for a particular action and hence reduces inter-action similarity. Then, a sparsity-based
classification method is proposed to classify the low-rank representation of clips obtained
using these dictionaries. We show that this representation based on dictionary learning improves
the classification performance across actions. Also, a few of the actions consist of
rapid body deformations that hinder the extraction of local features from body movements.
Hence, we propose to use a dictionary which is trained on convolutional neural network
(CNN) features of the human body in various poses to reliably identify actors from the
background. Particularly, we demonstrate the efficacy of sparse representation in the identification
of the human body under rapid and substantial deformation.
In the first two approaches, sparsity-based representation is developed to improve discriminability
using class-specific dictionaries that utilize action labels. However, developing
an unsupervised representation of actions is more beneficial as it can be used to both
recognize similar actions and localize actions. We propose to exploit inter-action similarity
to train a universal attribute model (UAM) in order to learn action attributes (common and
distinct) implicitly across all the actions. Using maximum aposteriori (MAP) adaptation,
a high-dimensional super action-vector (SAV) for each clip is extracted. As this SAV contains
redundant attributes of all other actions, we use factor analysis to extract a novel lowvi
dimensional action-vector representation for each clip. Action-vectors are shown to suppress
background motion and highlight actions of interest in both trimmed and untrimmed
clips that contributes to action recognition without the help of any classifiers.
It is observed during our experiments that action-vector cannot effectively discriminate
between actions which are visually similar to each other. Hence, we subject action-vectors
to supervised linear embedding using linear discriminant analysis (LDA) and probabilistic
LDA (PLDA) to enforce discrimination. Particularly, we show that leveraging complimentary
information across action-vectors using different local features followed by discriminative
embedding provides the best classification performance. Further, we explore
non-linear embedding of action-vectors using Siamese networks especially for fine-grained
action recognition. A visualization of the hidden layer output in Siamese networks shows
its ability to effectively separate visually similar actions. This leads to better classification
performance than linear embedding on fine-grained action recognition.
All of the above approaches are presented on large unconstrained datasets with hundreds
of examples per action. However, actions in surveillance videos like snatch thefts are
difficult to model because of the diverse variety of scenarios in which they occur and very
few labeled examples. Hence, we propose to utilize the universal attribute model (UAM)
trained on large action datasets to represent such actions. Specifically, we show that there
are similarities between certain actions in the large datasets with snatch thefts which help
in extracting a representation for snatch thefts using the attributes from the UAM. This
representation is shown to be effective in distinguishing snatch thefts from regular actions
with high accuracy.In summary, this thesis proposes both supervised and unsupervised approaches for representing
actions which provide better discrimination than existing representations. The
first approach presents a dictionary learning based sparse representation for effective discrimination
of actions. Also, we propose a sparse representation for the human body based
on dictionaries in order to recognize actions with rapid body deformations. In the next
approach, a low-dimensional representation called action-vector for unsupervised action
recognition is presented. Further, linear and non-linear embedding of action-vectors is
proposed for addressing inter-action similarity and fine-grained action recognition, respectively.
Finally, we propose a representation for locating snatch thefts among thousands of
regular interactions in surveillance videos
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