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| Author | Zhengying Liu; Adrien Pavao; Zhen Xu; Sergio Escalera; Fabio Ferreira; Isabelle Guyon; Sirui Hong; Frank Hutter; Rongrong Ji; Julio C. S. Jacques Junior; Ge Li; Marius Lindauer; Zhipeng Luo; Meysam Madadi; Thomas Nierhoff; Kangning Niu; Chunguang Pan; Danny Stoll; Sebastien Treguer; Jin Wang; Peng Wang; Chenglin Wu; Youcheng Xiong; Arber Zela; Yang Zhang | ||||
| Title | Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019 | Type | Journal Article | ||
| Year | 2021 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
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43 | Issue | 9 | Pages | 3108 - 3125 |
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| Abstract | This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series, which helped sorting out a profusion of AutoML solutions for Deep Learning (DL) that had been introduced in a variety of settings, but lacked fair comparisons. All input data modalities (time series, images, videos, text, tabular) were formatted as tensors and all tasks were multi-label classification problems. Code submissions were executed on hidden tasks, with limited time and computational resources, pushing solutions that get results quickly. In this setting, DL methods dominated, though popular Neural Architecture Search (NAS) was impractical. Solutions relied on fine-tuned pre-trained networks, with architectures matching data modality. Post-challenge tests did not reveal improvements beyond the imposed time limit. While no component is particularly original or novel, a high level modular organization emerged featuring a “meta-learner”, “data ingestor”, “model selector”, “model/learner”, and “evaluator”. This modularity enabled ablation studies, which revealed the importance of (off-platform) meta-learning, ensembling, and efficient data management. Experiments on heterogeneous module combinations further confirm the (local) optimality of the winning solutions. Our challenge legacy includes an ever-lasting benchmark (http://autodl.chalearn.org), the open-sourced code of the winners, and a free “AutoDL self-service.” | ||||
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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ LPX2021 | Serial | 3587 | ||
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| Author | Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez | ||||
| Title | Rank Estimation in 3D Multibody Motion Segmentation | Type | Journal Article | ||
| Year | 2008 | Publication | Electronic Letters | Abbreviated Journal | |
| Volume |
44 | Issue | 4 | Pages | 279-280 |
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| Abstract | A novel technique for rank estimation in 3D multibody motion segmentation is proposed. It is based on the study of the frequency spectra of moving rigid objects and does not use or assume a prior knowledge of the objects contained in the scene (i.e. number of objects and motion). The significance of rank estimation on multibody motion segmentation results is shown by using two motion segmentation algorithms over both synthetic and real data. | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ JSL2008a | Serial | 939 | ||
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| Author | Debora Gil; Aura Hernandez-Sabate; Mireia Brunat;Steven Jansen; Jordi Martinez-Vilalta | ||||
| Title | Structure-preserving smoothing of biomedical images | Type | Journal Article | ||
| Year | 2011 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
44 | Issue | 9 | Pages | 1842-1851 |
| Keywords | Non-linear smoothing; Differential geometry; Anatomical structures; segmentation; Cardiac magnetic resonance; Computerized tomography | ||||
| Abstract | Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood. In this contribution, we use differential geometry concepts to define a diffusion operator that restricts to image consistent level-sets. In this manner, the final state is a non-uniform intensity image presenting homogeneous inter-tissue transitions along anatomical structures, while smoothing intra-structure texture. Experiments on different types of medical images (magnetic resonance, computerized tomography) illustrate its benefit on a further process (such as segmentation) of images. | ||||
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| ISSN | 0031-3203 | ISBN | Medium | ||
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| Notes | IAM; ADAS | Approved | no | ||
| Call Number | IAM @ iam @ GHB2011 | Serial | 1526 | ||
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| Author | Palaiahnakote Shivakumara; Anjan Dutta; Trung Quy Phan; Chew Lim Tan; Umapada Pal | ||||
| Title | A Novel Mutual Nearest Neighbor based Symmetry for Text Frame Classification in Video | Type | Journal Article | ||
| Year | 2011 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
44 | Issue | 8 | Pages | 1671-1683 |
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| Abstract | In the field of multimedia retrieval in video, text frame classification is essential for text detection, event detection, event boundary detection, etc. We propose a new text frame classification method that introduces a combination of wavelet and median moment with k-means clustering to select probable text blocks among 16 equally sized blocks of a video frame. The same feature combination is used with a new Max–Min clustering at the pixel level to choose probable dominant text pixels in the selected probable text blocks. For the probable text pixels, a so-called mutual nearest neighbor based symmetry is explored with a four-quadrant formation centered at the centroid of the probable dominant text pixels to know whether a block is a true text block or not. If a frame produces at least one true text block then it is considered as a text frame otherwise it is a non-text frame. Experimental results on different text and non-text datasets including two public datasets and our own created data show that the proposed method gives promising results in terms of recall and precision at the block and frame levels. Further, we also show how existing text detection methods tend to misclassify non-text frames as text frames in term of recall and precision at both the block and frame levels. | ||||
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ SDP2011 | Serial | 1727 | ||
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| Author | Carlo Gatta; Eloi Puertas; Oriol Pujol | ||||
| Title | Multi-Scale Stacked Sequential Learning | Type | Journal Article | ||
| Year | 2011 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
44 | Issue | 10-11 | Pages | 2414-2416 |
| Keywords | Stacked sequential learning; Multiscale; Multiresolution; Contextual classification | ||||
| Abstract | One of the most widely used assumptions in supervised learning is that data is independent and identically distributed. This assumption does not hold true in many real cases. Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring examples exhibit some kind of relationship. In the literature, there are different approaches that try to capture and exploit this correlation, by means of different methodologies. In this paper we focus on meta-learning strategies and, in particular, the stacked sequential learning approach. The main contribution of this work is two-fold: first, we generalize the stacked sequential learning. This generalization reflects the key role of neighboring interactions modeling. Second, we propose an effective and efficient way of capturing and exploiting sequential correlations that takes into account long-range interactions by means of a multi-scale pyramidal decomposition of the predicted labels. Additionally, this new method subsumes the standard stacked sequential learning approach. We tested the proposed method on two different classification tasks: text lines classification in a FAQ data set and image classification. Results on these tasks clearly show that our approach outperforms the standard stacked sequential learning. Moreover, we show that the proposed method allows to control the trade-off between the detail and the desired range of the interactions. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Notes | MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ GPP2011 | Serial | 1802 | ||
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| Author | Partha Pratim Roy; Umapada Pal; Josep Llados | ||||
| Title | Document Seal Detection Using Ght and Character Proximity Graphs | Type | Journal Article | ||
| Year | 2011 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
44 | Issue | 6 | Pages | 1282-1295 |
| Keywords | Seal recognition; Graphical symbol spotting; Generalized Hough transform; Multi-oriented character recognition | ||||
| Abstract | This paper deals with automatic detection of seal (stamp) from documents with cluttered background. Seal detection involves a difficult challenge due to its multi-oriented nature, arbitrary shape, overlapping of its part with signature, noise, etc. Here, a seal object is characterized by scale and rotation invariant spatial feature descriptors computed from recognition result of individual connected components (characters). Scale and rotation invariant features are used in a Support Vector Machine (SVM) classifier to recognize multi-scale and multi-oriented text characters. The concept of generalized Hough transform (GHT) is used to detect the seal and a voting scheme is designed for finding possible location of the seal in a document based on the spatial feature descriptor of neighboring component pairs. The peak of votes in GHT accumulator validates the hypothesis to locate the seal in a document. Experiment is performed in an archive of historical documents of handwritten/printed English text. Experimental results show that the method is robust in locating seal instances of arbitrary shape and orientation in documents, and also efficient in indexing a collection of documents for retrieval purposes. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ RPL2011 | Serial | 1820 | ||
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| Author | Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Ludmila I. Kuncheva | ||||
| Title | Occlusion handling via random subspace classifiers for human detection | Type | Journal Article | ||
| Year | 2014 | Publication | IEEE Transactions on Systems, Man, and Cybernetics (Part B) | Abbreviated Journal | TSMCB |
| Volume |
44 | Issue | 3 | Pages | 342-354 |
| Keywords | Pedestriand Detection; occlusion handling | ||||
| Abstract | This paper describes a general method to address partial occlusions for human detection in still images. The Random Subspace Method (RSM) is chosen for building a classifier ensemble robust against partial occlusions. The component classifiers are chosen on the basis of their individual and combined performance. The main contribution of this work lies in our approach’s capability to improve the detection rate when partial occlusions are present without compromising the detection performance on non occluded data. In contrast to many recent approaches, we propose a method which does not require manual labelling of body parts, defining any semantic spatial components, or using additional data coming from motion or stereo. Moreover, the method can be easily extended to other object classes. The experiments are performed on three large datasets: the INRIA person dataset, the Daimler Multicue dataset, and a new challenging dataset, called PobleSec, in which a considerable number of targets are partially occluded. The different approaches are evaluated at the classification and detection levels for both partially occluded and non-occluded data. The experimental results show that our detector outperforms state-of-the-art approaches in the presence of partial occlusions, while offering performance and reliability similar to those of the holistic approach on non-occluded data. The datasets used in our experiments have been made publicly available for benchmarking purposes | ||||
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| ISSN | 2168-2267 | ISBN | Medium | ||
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| Notes | ADAS; 605.203; 600.057; 600.054; 601.042; 601.187; 600.076 | Approved | no | ||
| Call Number | ADAS @ adas @ MVL2014 | Serial | 2213 | ||
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| Author | Oscar Lopes; Miguel Reyes; Sergio Escalera; Jordi Gonzalez | ||||
| Title | Spherical Blurred Shape Model for 3-D Object and Pose Recognition: Quantitative Analysis and HCI Applications in Smart Environments | Type | Journal Article | ||
| Year | 2014 | Publication | IEEE Transactions on Systems, Man and Cybernetics (Part B) | Abbreviated Journal | TSMCB |
| Volume |
44 | Issue | 12 | Pages | 2379-2390 |
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| Abstract | The use of depth maps is of increasing interest after the advent of cheap multisensor devices based on structured light, such as Kinect. In this context, there is a strong need of powerful 3-D shape descriptors able to generate rich object representations. Although several 3-D descriptors have been already proposed in the literature, the research of discriminative and computationally efficient descriptors is still an open issue. In this paper, we propose a novel point cloud descriptor called spherical blurred shape model (SBSM) that successfully encodes the structure density and local variabilities of an object based on shape voxel distances and a neighborhood propagation strategy. The proposed SBSM is proven to be rotation and scale invariant, robust to noise and occlusions, highly discriminative for multiple categories of complex objects like the human hand, and computationally efficient since the SBSM complexity is linear to the number of object voxels. Experimental evaluation in public depth multiclass object data, 3-D facial expressions data, and a novel hand poses data sets show significant performance improvements in relation to state-of-the-art approaches. Moreover, the effectiveness of the proposal is also proved for object spotting in 3-D scenes and for real-time automatic hand pose recognition in human computer interaction scenarios. | ||||
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| ISSN | 2168-2267 | ISBN | Medium | ||
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| Notes | HuPBA; ISE; 600.078;MILAB | Approved | no | ||
| Call Number | Admin @ si @ LRE2014 | Serial | 2442 | ||
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| Author | Beata Megyesi; Bernhard Esslinger; Alicia Fornes; Nils Kopal; Benedek Lang; George Lasry; Karl de Leeuw; Eva Pettersson; Arno Wacker; Michelle Waldispuhl | ||||
| Title | Decryption of historical manuscripts: the DECRYPT project | Type | Journal Article | ||
| Year | 2020 | Publication | Cryptologia | Abbreviated Journal | CRYPT |
| Volume |
44 | Issue | 6 | Pages | 545-559 |
| Keywords | automatic decryption; cipher collection; historical cryptology; image transcription | ||||
| Abstract | Many historians and linguists are working individually and in an uncoordinated fashion on the identification and decryption of historical ciphers. This is a time-consuming process as they often work without access to automatic methods and processes that can accelerate the decipherment. At the same time, computer scientists and cryptologists are developing algorithms to decrypt various cipher types without having access to a large number of original ciphertexts. In this paper, we describe the DECRYPT project aiming at the creation of resources and tools for historical cryptology by bringing the expertise of various disciplines together for collecting data, exchanging methods for faster progress to transcribe, decrypt and contextualize historical encrypted manuscripts. We present our goals and work-in progress of a general approach for analyzing historical encrypted manuscripts using standardized methods and a new set of state-of-the-art tools. We release the data and tools as open-source hoping that all mentioned disciplines would benefit and contribute to the research infrastructure of historical cryptology. | ||||
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| Notes | DAG; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ MEF2020 | Serial | 3347 | ||
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| Author | Mohamed Ali Souibgui; Y.Kessentini | ||||
| Title | DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement | Type | Journal Article | ||
| Year | 2022 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume |
44 | Issue | 3 | Pages | 1180-1191 |
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| Abstract | Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system. In this paper, we propose an effective end-to-end framework named Document Enhancement Generative Adversarial Networks (DE-GAN) that uses the conditional GANs (cGANs) to restore severely degraded document images. To the best of our knowledge, this practice has not been studied within the context of generative adversarial deep networks. We demonstrate that, in different tasks (document clean up, binarization, deblurring and watermark removal), DE-GAN can produce an enhanced version of the degraded document with a high quality. In addition, our approach provides consistent improvements compared to state-of-the-art methods over the widely used DIBCO 2013, DIBCO 2017 and H-DIBCO 2018 datasets, proving its ability to restore a degraded document image to its ideal condition. The obtained results on a wide variety of degradation reveal the flexibility of the proposed model to be exploited in other document enhancement problems. | ||||
| Address | 1 March 2022 | ||||
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| Notes | DAG; 602.230; 600.121; 600.140 | Approved | no | ||
| Call Number | Admin @ si @ SoK2022 | Serial | 3454 | ||
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| Author | Noha Elfiky; Fahad Shahbaz Khan; Joost Van de Weijer; Jordi Gonzalez | ||||
| Title | Discriminative Compact Pyramids for Object and Scene Recognition | Type | Journal Article | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
45 | Issue | 4 | Pages | 1627-1636 |
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| Abstract | Spatial pyramids have been successfully applied to incorporating spatial information into bag-of-words based image representation. However, a major drawback is that it leads to high dimensional image representations. In this paper, we present a novel framework for obtaining compact pyramid representation. First, we investigate the usage of the divisive information theoretic feature clustering (DITC) algorithm in creating a compact pyramid representation. In many cases this method allows us to reduce the size of a high dimensional pyramid representation up to an order of magnitude with little or no loss in accuracy. Furthermore, comparison to clustering based on agglomerative information bottleneck (AIB) shows that our method obtains superior results at significantly lower computational costs. Moreover, we investigate the optimal combination of multiple features in the context of our compact pyramid representation. Finally, experiments show that the method can obtain state-of-the-art results on several challenging data sets. | ||||
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| ISSN | 0031-3203 | ISBN | Medium | ||
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| Notes | ISE; CAT;CIC | Approved | no | ||
| Call Number | Admin @ si @ EKW2012 | Serial | 1807 | ||
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| Author | Bogdan Raducanu; Fadi Dornaika | ||||
| Title | A Supervised Non-linear Dimensionality Reduction Approach for Manifold Learning | Type | Journal Article | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
45 | Issue | 6 | Pages | 2432-2444 |
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| Abstract | IF= 2.61
IF=2.61 (2010) In this paper we introduce a novel supervised manifold learning technique called Supervised Laplacian Eigenmaps (S-LE), which makes use of class label information to guide the procedure of non-linear dimensionality reduction by adopting the large margin concept. The graph Laplacian is split into two components: within-class graph and between-class graph to better characterize the discriminant property of the data. Our approach has two important characteristics: (i) it adaptively estimates the local neighborhood surrounding each sample based on data density and similarity and (ii) the objective function simultaneously maximizes the local margin between heterogeneous samples and pushes the homogeneous samples closer to each other. Our approach has been tested on several challenging face databases and it has been conveniently compared with other linear and non-linear techniques, demonstrating its superiority. Although we have concentrated in this paper on the face recognition problem, the proposed approach could also be applied to other category of objects characterized by large variations in their appearance (such as hand or body pose, for instance. |
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| ISSN | 0031-3203 | ISBN | Medium | ||
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| Notes | OR; MV | Approved | no | ||
| Call Number | Admin @ si @ RaD2012a | Serial | 1884 | ||
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| Author | Jon Almazan; Alicia Fornes; Ernest Valveny | ||||
| Title | A non-rigid appearance model for shape description and recognition | Type | Journal Article | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
45 | Issue | 9 | Pages | 3105--3113 |
| Keywords | Shape recognition; Deformable models; Shape modeling; Hand-drawn recognition | ||||
| 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. | ||||
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| ISSN | 0031-3203 | ISBN | Medium | ||
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| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ AFV2012 | Serial | 1982 | ||
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| Author | Jaume Gibert; Ernest Valveny; Horst Bunke | ||||
| Title | Graph Embedding in Vector Spaces by Node Attribute Statistics | Type | Journal Article | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
45 | Issue | 9 | Pages | 3072-3083 |
| Keywords | Structural pattern recognition; Graph embedding; Data clustering; Graph classification | ||||
| Abstract | Graph-based representations are of broad use and applicability in pattern recognition. They exhibit, however, a major drawback with regards to the processing tools that are available in their domain. Graphembedding into vectorspaces is a growing field among the structural pattern recognition community which aims at providing a feature vector representation for every graph, and thus enables classical statistical learning machinery to be used on graph-based input patterns. In this work, we propose a novel embedding methodology for graphs with continuous nodeattributes and unattributed edges. The approach presented in this paper is based on statistics of the node labels and the edges between them, based on their similarity to a set of representatives. We specifically deal with an important issue of this methodology, namely, the selection of a suitable set of representatives. In an experimental evaluation, we empirically show the advantages of this novel approach in the context of different classification problems using several databases of graphs. | ||||
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| ISSN | 0031-3203 | ISBN | Medium | ||
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ GVB2012a | Serial | 1992 | ||
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| Author | Jorge Bernal; F. Javier Sanchez; Fernando Vilariño | ||||
| Title | Towards Automatic Polyp Detection with a Polyp Appearance Model | Type | Journal Article | ||
| Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume |
45 | Issue | 9 | Pages | 3166-3182 |
| Keywords | Colonoscopy,PolypDetection,RegionSegmentation,SA-DOVA descriptot | ||||
| Abstract | This work aims at the automatic polyp detection by using a model of polyp appearance in the context of the analysis of colonoscopy videos. Our method consists of three stages: region segmentation, region description and region classification. The performance of our region segmentation method guarantees that if a polyp is present in the image, it will be exclusively and totally contained in a single region. The output of the algorithm also defines which regions can be considered as non-informative. We define as our region descriptor the novel Sector Accumulation-Depth of Valleys Accumulation (SA-DOVA), which provides a necessary but not sufficient condition for the polyp presence. Finally, we classify our segmented regions according to the maximal values of the SA-DOVA descriptor. Our preliminary classification results are promising, especially when classifying those parts of the image that do not contain a polyp inside. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| ISSN | 0031-3203 | ISBN | Medium | ||
| Area | 800 | Expedition | Conference | IbPRIA | |
| Notes | MV;SIAI | Approved | no | ||
| Call Number | Admin @ si @ BSV2012; IAM @ iam | Serial | 1997 | ||
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