Jaume Gibert, Ernest Valveny, & Horst Bunke. (2013). Embedding of Graphs with Discrete Attributes Via Label Frequencies. IJPRAI - International Journal of Pattern Recognition and Artificial Intelligence, 27(3), 1360002–1360029.
Abstract: Graph-based representations of patterns are very flexible and powerful, but they are not easily processed due to the lack of learning algorithms in the domain of graphs. Embedding a graph into a vector space solves this problem since graphs are turned into feature vectors and thus all the statistical learning machinery becomes available for graph input patterns. In this work we present a new way of embedding discrete attributed graphs into vector spaces using node and edge label frequencies. The methodology is experimentally tested on graph classification problems, using patterns of different nature, and it is shown to be competitive to state-of-the-art classification algorithms for graphs, while being computationally much more efficient.
Keywords: Discrete attributed graphs; graph embedding; graph classification
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Muhammad Anwer Rao. (2013). Color for Object Detection and Action Recognition (Antonio Lopez, & Joost Van de Weijer, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Recognizing object categories in real world images is a challenging problem in computer vision. The deformable part based framework is currently the most successful approach for object detection. Generally, HOG are used for image representation within the part-based framework. For action recognition, the bag-of-word framework has shown to provide promising results. Within the bag-of-words framework, local image patches are described by SIFT descriptor. Contrary to object detection and action recognition, combining color and shape has shown to provide the best performance for object and scene recognition.
In the first part of this thesis, we analyze the problem of person detection in still images. Standard person detection approaches rely on intensity based features for image representation while ignoring the color. Channel based descriptors is one of the most commonly used approaches in object recognition. This inspires us to evaluate incorporating color information using the channel based fusion approach for the task of person detection.
In the second part of the thesis, we investigate the problem of object detection in still images. Due to high dimensionality, channel based fusion increases the computational cost. Moreover, channel based fusion has been found to obtain inferior results for object category where one of the visual varies significantly. On the other hand, late fusion is known to provide improved results for a wide range of object categories. A consequence of late fusion strategy is the need of a pure color descriptor. Therefore, we propose to use Color attributes as an explicit color representation for object detection. Color attributes are compact and computationally efficient. Consequently color attributes are combined with traditional shape features providing excellent results for object detection task.
Finally, we focus on the problem of action detection and classification in still images. We investigate the potential of color for action classification and detection in still images. We also evaluate different fusion approaches for combining color and shape information for action recognition. Additionally, an analysis is performed to validate the contribution of color for action recognition. Our results clearly demonstrate that combining color and shape information significantly improve the performance of both action classification and detection in still images.
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Javier Marin. (2013). Pedestrian Detection Based on Local Experts (Antonio Lopez, & Jaume Amores, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: During the last decade vision-based human detection systems have started to play a key rolein multiple applications linked to driver assistance, surveillance, robot sensing and home automation.
Detecting humans is by far one of the most challenging tasks in Computer Vision.
This is mainly due to the high degree of variability in the human appearanceassociated to
the clothing, pose, shape and size. Besides, other factors such as cluttered scenarios, partial occlusions, or environmental conditions can make the detection task even harder.
Most promising methods of the state-of-the-art rely on discriminative learning paradigms which are fed with positive and negative examples. The training data is one of the most
relevant elements in order to build a robust detector as it has to cope the large variability of the target. In order to create this dataset human supervision is required. The drawback at this point is the arduous effort of annotating as well as looking for such claimed variability.
In this PhD thesis we address two recurrent problems in the literature. In the first stage,we aim to reduce the consuming task of annotating, namely, by using computer graphics.
More concretely, we develop a virtual urban scenario for later generating a pedestrian dataset.
Then, we train a detector using this dataset, and finally we assess if this detector can be successfully applied in a real scenario.
In the second stage, we focus on increasing the robustness of our pedestrian detectors
under partial occlusions. In particular, we present a novel occlusion handling approach to increase the performance of block-based holistic methods under partial occlusions. For this purpose, we make use of local experts via a RandomSubspaceMethod (RSM) to handle these cases. If the method infers a possible partial occlusion, then the RSM, based on performance statistics obtained from partially occluded data, is applied. The last objective of this thesis
is to propose a robust pedestrian detector based on an ensemble of local experts. To achieve this goal, we use the random forest paradigm, where the trees act as ensembles an their nodesare the local experts. In particular, each expert focus on performing a robust classification ofa pedestrian body patch. This approach offers computational efficiency and far less design complexity when compared to other state-of-the-artmethods, while reaching better accuracy
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Wenjuan Gong. (2013). 3D Motion Data aided Human Action Recognition and Pose Estimation (Jordi Gonzalez, & Xavier Roca, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: In this work, we explore human action recognition and pose estimation prob-
lems. Different from traditional works of learning from 2D images or video
sequences and their annotated output, we seek to solve the problems with ad-
ditional 3D motion capture information, which helps to fill the gap between 2D
image features and human interpretations.
We first compare two different schools of approaches commonly used for 3D
pose estimation from 2D pose configuration: modeling and learning methods.
By looking into experiments results and considering our problems, we fixed a
learning method as the following approaches to do pose estimation. We then
establish a framework by adding a module of detecting 2D pose configuration
from images with varied background, which widely extend the application of
the approach. We also seek to directly estimate 3D poses from image features,
instead of estimating 2D poses as a intermediate module. We explore a robust
input feature, which combined with the proposed distance measure, provides
a solution for noisy or corrupted inputs. We further utilize the above method
to estimate weak poses,which is a concise representation of the original poses
by using dimension deduction technologies, from image features. Weak pose
space is where we calculate vocabulary and label action types using a bog of
words pipeline. Temporal information of an action is taken into consideration by
considering several consecutive frames as a single unit for computing vocabulary
and histogram assignments.
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Murad Al Haj. (2013). Looking at Faces: Detection, Tracking and Pose Estimation (Jordi Gonzalez, & Xavier Roca, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Humans can effortlessly perceive faces, follow them over space and time, and decode their rich content, such as pose, identity and expression. However, despite many decades of research on automatic facial perception in areas like face detection, expression recognition, pose estimation and face recognition, and despite many successes, a complete solution remains elusive. This thesis is dedicated to three problems in automatic face perception, namely face detection, face tracking and pose estimation.
In face detection, an initial simple model is presented that uses pixel-based heuristics to segment skin locations and hand-crafted rules to determine the locations of the faces present in an image. Different colorspaces are studied to judge whether a colorspace transformation can aid skin color detection. The output of this study is used in the design of a more complex face detector that is able to successfully generalize to different scenarios.
In face tracking, a framework that combines estimation and control in a joint scheme is presented to track a face with a single pan-tilt-zoom camera. While this work is mainly motivated by tracking faces, it can be easily applied atop of any detector to track different objects. The applicability of this method is demonstrated on simulated as well as real-life scenarios.
The last and most important part of this thesis is dedicate to monocular head pose estimation. In this part, a method based on partial least squares (PLS) regression is proposed to estimate pose and solve the alignment problem simultaneously. The contributions of this work are two-fold: 1) demonstrating that the proposed method achieves better than state-of-the-art results on the estimation problem and 2) developing a technique to reduce misalignment based on the learned PLS factors that outperform multiple instance learning (MIL) without the need for any re-training or the inclusion of misaligned samples in the training process, as normally done in MIL.
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Albert Gordo. (2013). Document Image Representation, Classification and Retrieval in Large-Scale Domains (Ernest Valveny, & Florent Perronnin, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Despite the “paperless office” ideal that started in the decade of the seventies, businesses still strive against an increasing amount of paper documentation. Companies still receive huge amounts of paper documentation that need to be analyzed and processed, mostly in a manual way. A solution for this task consists in, first, automatically scanning the incoming documents. Then, document images can be analyzed and information can be extracted from the data. Documents can also be automatically dispatched to the appropriate workflows, used to retrieve similar documents in the dataset to transfer information, etc.
Due to the nature of this “digital mailroom”, we need document representation methods to be general, i.e., able to cope with very different types of documents. We need the methods to be sound, i.e., able to cope with unexpected types of documents, noise, etc. And, we need to methods to be scalable, i.e., able to cope with thousands or millions of documents that need to be processed, stored, and consulted. Unfortunately, current techniques of document representation, classification and retrieval are not apt for this digital mailroom framework, since they do not fulfill some or all of these requirements.
Through this thesis we focus on the problem of document representation aimed at classification and retrieval tasks under this digital mailroom framework. We first propose a novel document representation based on runlength histograms, and extend it to cope with more complex documents such as multiple-page documents, or documents that contain more sources of information such as extracted OCR text. Then we focus on the scalability requirements and propose a novel binarization method which we dubbed PCAE, as well as two general asymmetric distances between binary embeddings that can significantly improve the retrieval results at a minimal extra computational cost. Finally, we note the importance of supervised learning when performing large-scale retrieval, and study several approaches that can significantly boost the results at no extra cost at query time.
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Muhammad Muzzamil Luqman, Jean-Yves Ramel, & Josep Llados. (2013). Multilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces. In Graph Embedding for Pattern Analysis (pp. 1–26). Springer New York.
Abstract: Ability to recognize patterns is among the most crucial capabilities of human beings for their survival, which enables them to employ their sophisticated neural and cognitive systems [1], for processing complex audio, visual, smell, touch, and taste signals. Man is the most complex and the best existing system of pattern recognition. Without any explicit thinking, we continuously compare, classify, and identify huge amount of signal data everyday [2], starting from the time we get up in the morning till the last second we fall asleep. This includes recognizing the face of a friend in a crowd, a spoken word embedded in noise, the proper key to lock the door, smell of coffee, the voice of a favorite singer, the recognition of alphabetic characters, and millions of more tasks that we perform on regular basis.
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Jean-Marc Ogier, Wenyin Liu, & Josep Llados (Eds.). (2010). Graphics Recognition: Achievements, Challenges, and Evolution (Vol. 6020). LNCS. Springer Link.
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Isabel Guitart, Jordi Conesa, Luis Villarejo, Agata Lapedriza, David Masip, Antoni Perez, et al. (2013). Opinion Mining on Educational Resources at the Open University of Catalonia. In 3rd International Workshop on Adaptive Learning via Interactive, Collaborative and Emotional approaches. In conjunction with CISIS 2013: The 7th International Conference on Complex, Intelligent, and Software Intensive Systems (pp. 385–390).
Abstract: In order to make improvements to teaching, it is vital to know what students think of the way they are taught. With that purpose in mind, exhaustively analyzing the forums associated with the subjects taught at the Universitat Oberta de Cataluya (UOC) would be extremely helpful, as the university's students often post comments on their learning experiences in them. Exploiting the content of such forums is not a simple undertaking. The volume of data involved is very large, and performing the task manually would require a great deal of effort from lecturers. As a first step to solve this problem, we propose a tool to automatically analyze the posts in forums of communities of UOC students and teachers, with a view to systematically mining the opinions they contain. This article defines the architecture of such tool and explains how lexical-semantic and language technology resources can be used to that end. For pilot testing purposes, the tool has been used to identify students' opinions on the UOC's Business Intelligence master's degree course during the last two years. The paper discusses the results of such test. The contribution of this paper is twofold. Firstly, it demonstrates the feasibility of using natural language parsing techniques to help teachers to make decisions. Secondly, it introduces a simple tool that can be refined and adapted to a virtual environment for the purpose in question.
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Jose Manuel Alvarez, Theo Gevers, Ferran Diego, & Antonio Lopez. (2013). Road Geometry Classification by Adaptative Shape Models. TITS - IEEE Transactions on Intelligent Transportation Systems, 14(1), 459–468.
Abstract: Vision-based road detection is important for different applications in transportation, such as autonomous driving, vehicle collision warning, and pedestrian crossing detection. Common approaches to road detection are based on low-level road appearance (e.g., color or texture) and neglect of the scene geometry and context. Hence, using only low-level features makes these algorithms highly depend on structured roads, road homogeneity, and lighting conditions. Therefore, the aim of this paper is to classify road geometries for road detection through the analysis of scene composition and temporal coherence. Road geometry classification is proposed by building corresponding models from training images containing prototypical road geometries. We propose adaptive shape models where spatial pyramids are steered by the inherent spatial structure of road images. To reduce the influence of lighting variations, invariant features are used. Large-scale experiments show that the proposed road geometry classifier yields a high recognition rate of 73.57% ± 13.1, clearly outperforming other state-of-the-art methods. Including road shape information improves road detection results over existing appearance-based methods. Finally, it is shown that invariant features and temporal information provide robustness against disturbing imaging conditions.
Keywords: road detection
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Marçal Rusiñol, R.Roset, Josep Llados, & C.Montaner. (2011). Automatic Index Generation of Digitized Map Series by Coordinate Extraction and Interpretation. In In Proceedings of the Sixth International Workshop on Digital Technologies in Cartographic Heritage.
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Yainuvis Socarras, David Vazquez, Antonio Lopez, David Geronimo, & Theo Gevers. (2012). Improving HOG with Image Segmentation: Application to Human Detection. In J. Blanc-Talon et al. (Ed.), 11th International Conference on Advanced Concepts for Intelligent Vision Systems (Vol. 7517, pp. 178–189). LNCS. Springer Berlin Heidelberg.
Abstract: In this paper we improve the histogram of oriented gradients (HOG), a core descriptor of state-of-the-art object detection, by the use of higher-level information coming from image segmentation. The idea is to re-weight the descriptor while computing it without increasing its size. The benefits of the proposal are two-fold: (i) to improve the performance of the detector by enriching the descriptor information and (ii) take advantage of the information of image segmentation, which in fact is likely to be used in other stages of the detection system such as candidate generation or refinement.
We test our technique in the INRIA person dataset, which was originally developed to test HOG, embedding it in a human detection system. The well-known segmentation method, mean-shift (from smaller to larger super-pixels), and different methods to re-weight the original descriptor (constant, region-luminance, color or texture-dependent) has been evaluated. We achieve performance improvements of 4:47% in detection rate through the use of differences of color between contour pixel neighborhoods as re-weighting function.
Keywords: Segmentation; Pedestrian Detection
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Jon Almazan, Alicia Fornes, & Ernest Valveny. (2012). A non-rigid appearance model for shape description and recognition. PR - Pattern Recognition, 45(9), 3105–3113.
Abstract: In this paper we describe a framework to learn a model of shape variability in a set of patterns. The framework is based on the Active Appearance Model (AAM) and permits to combine shape deformations with appearance variability. We have used two modifications of the Blurred Shape Model (BSM) descriptor as basic shape and appearance features to learn the model. These modifications permit to overcome the rigidity of the original BSM, adapting it to the deformations of the shape to be represented. We have applied this framework to representation and classification of handwritten digits and symbols. We show that results of the proposed methodology outperform the original BSM approach.
Keywords: Shape recognition; Deformable models; Shape modeling; Hand-drawn recognition
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Jon Almazan, David Fernandez, Alicia Fornes, Josep Llados, & Ernest Valveny. (2012). A Coarse-to-Fine Approach for Handwritten Word Spotting in Large Scale Historical Documents Collection. In 13th International Conference on Frontiers in Handwriting Recognition (pp. 453–458).
Abstract: In this paper we propose an approach for word spotting in handwritten document images. We state the problem from a focused retrieval perspective, i.e. locating instances of a query word in a large scale dataset of digitized manuscripts. We combine two approaches, namely one based on word segmentation and another one segmentation-free. The first approach uses a hashing strategy to coarsely prune word images that are unlikely to be instances of the query word. This process is fast but has a low precision due to the errors introduced in the segmentation step. The regions containing candidate words are sent to the second process based on a state of the art technique from the visual object detection field. This discriminative model represents the appearance of the query word and computes a similarity score. In this way we propose a coarse-to-fine approach achieving a compromise between efficiency and accuracy. The validation of the model is shown using a collection of old handwritten manuscripts. We appreciate a substantial improvement in terms of precision regarding the previous proposed method with a low computational cost increase.
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Jon Almazan, Albert Gordo, Alicia Fornes, & Ernest Valveny. (2012). Efficient Exemplar Word Spotting. In 23rd British Machine Vision Conference (67.pp. 1–67.11).
Abstract: In this paper we propose an unsupervised segmentation-free method for word spotting in document images.
Documents are represented with a grid of HOG descriptors, and a sliding window approach is used to locate the document regions that are most similar to the query. We use the exemplar SVM framework to produce a better representation of the query in an unsupervised way. Finally, the document descriptors are precomputed and compressed with Product Quantization. This offers two advantages: first, a large number of documents can be kept in RAM memory at the same time. Second, the sliding window becomes significantly faster since distances between quantized HOG descriptors can be precomputed. Our results significantly outperform other segmentation-free methods in the literature, both in accuracy and in speed and memory usage.
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