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Author |
Susana Alvarez; Maria Vanrell |
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Title |
Texton theory revisited: a bag-of-words approach to combine textons |
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2012 |
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Pattern Recognition |
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PR |
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45 |
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12 |
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4312-4325 |
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The aim of this paper is to revisit an old theory of texture perception and
update its computational implementation by extending it to colour. With this in mind we try to capture the optimality of perceptual systems. This is achieved in the proposed approach by sharing well-known early stages of the visual processes and extracting low-dimensional features that perfectly encode adequate properties for a large variety of textures without needing further learning stages. We propose several descriptors in a bag-of-words framework that are derived from different quantisation models on to the feature spaces. Our perceptual features are directly given by the shape and colour attributes of image blobs, which are the textons. In this way we avoid learning visual words and directly build the vocabularies on these lowdimensionaltexton spaces. Main differences between proposed descriptors rely on how co-occurrence of blob attributes is represented in the vocabularies. Our approach overcomes current state-of-art in colour texture description which is proved in several experiments on large texture datasets. |
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Admin @ si @ AlV2012a |
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2130 |
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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados; Mathieu Nicolas Delalandre |
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Title |
Multi-oriented touching text character segmentation in graphical documents using dynamic programming |
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Journal Article |
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2012 |
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Pattern Recognition |
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PR |
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45 |
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5 |
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1972-1983 |
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2,292 JCR
The touching character segmentation problem becomes complex when touching strings are multi-oriented. Moreover in graphical documents sometimes characters in a single-touching string have different orientations. Segmentation of such complex touching is more challenging. In this paper, we present a scheme towards the segmentation of English multi-oriented touching strings into individual characters. When two or more characters touch, they generate a big cavity region in the background portion. Based on the convex hull information, at first, we use this background information to find some initial points for segmentation of a touching string into possible primitives (a primitive consists of a single character or part of a character). Next, the primitives are merged to get optimum segmentation. A dynamic programming algorithm is applied for this purpose using the total likelihood of characters as the objective function. A SVM classifier is used to find the likelihood of a character. To consider multi-oriented touching strings the features used in the SVM are invariant to character orientation. Experiments were performed in different databases of real and synthetic touching characters and the results show that the method is efficient in segmenting touching characters of arbitrary orientations and sizes. |
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DAG |
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Admin @ si @ RPL2012a |
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2133 |
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Katerine Diaz; Aura Hernandez-Sabate; Antonio Lopez |
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Title |
A reduced feature set for driver head pose estimation |
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2016 |
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Applied Soft Computing |
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ASOC |
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45 |
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98-107 |
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Head pose estimation; driving performance evaluation; subspace based methods; linear regression |
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Evaluation of driving performance is of utmost importance in order to reduce road accident rate. Since driving ability includes visual-spatial and operational attention, among others, head pose estimation of the driver is a crucial indicator of driving performance. This paper proposes a new automatic method for coarse and fine head's yaw angle estimation of the driver. We rely on a set of geometric features computed from just three representative facial keypoints, namely the center of the eyes and the nose tip. With these geometric features, our method combines two manifold embedding methods and a linear regression one. In addition, the method has a confidence mechanism to decide if the classification of a sample is not reliable. The approach has been tested using the CMU-PIE dataset and our own driver dataset. Despite the very few facial keypoints required, the results are comparable to the state-of-the-art techniques. The low computational cost of the method and its robustness makes feasible to integrate it in massive consume devices as a real time application. |
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ADAS; 600.085; 600.076; |
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Admin @ si @ DHL2016 |
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2760 |
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Author |
Swathikiran Sudhakaran; Sergio Escalera; Oswald Lanz |
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Title |
Gate-Shift-Fuse for Video Action Recognition |
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Journal Article |
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2023 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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45 |
Issue |
9 |
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10913-10928 |
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Action Recognition; Video Classification; Spatial Gating; Channel Fusion |
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Convolutional Neural Networks are the de facto models for image recognition. However 3D CNNs, the straight forward extension of 2D CNNs for video recognition, have not achieved the same success on standard action recognition benchmarks. One of the main reasons for this reduced performance of 3D CNNs is the increased computational complexity requiring large scale annotated datasets to train them in scale. 3D kernel factorization approaches have been proposed to reduce the complexity of 3D CNNs. Existing kernel factorization approaches follow hand-designed and hard-wired techniques. In this paper we propose Gate-Shift-Fuse (GSF), a novel spatio-temporal feature extraction module which controls interactions in spatio-temporal decomposition and learns to adaptively route features through time and combine them in a data dependent manner. GSF leverages grouped spatial gating to decompose input tensor and channel weighting to fuse the decomposed tensors. GSF can be inserted into existing 2D CNNs to convert them into an efficient and high performing spatio-temporal feature extractor, with negligible parameter and compute overhead. We perform an extensive analysis of GSF using two popular 2D CNN families and achieve state-of-the-art or competitive performance on five standard action recognition benchmarks. |
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1 Sept. 2023 |
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HUPBA; no menciona |
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no |
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Admin @ si @ SEL2023 |
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3814 |
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Author |
Javier Selva; Anders S. Johansen; Sergio Escalera; Kamal Nasrollahi; Thomas B. Moeslund; Albert Clapes |
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Title |
Video transformers: A survey |
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Journal Article |
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Year |
2023 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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Volume |
45 |
Issue |
11 |
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12922-12943 |
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Artificial Intelligence; Computer Vision; Self-Attention; Transformers; Video Representations |
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Transformer models have shown great success handling long-range interactions, making them a promising tool for modeling video. However, they lack inductive biases and scale quadratically with input length. These limitations are further exacerbated when dealing with the high dimensionality introduced by the temporal dimension. While there are surveys analyzing the advances of Transformers for vision, none focus on an in-depth analysis of video-specific designs. In this survey, we analyze the main contributions and trends of works leveraging Transformers to model video. Specifically, we delve into how videos are handled at the input level first. Then, we study the architectural changes made to deal with video more efficiently, reduce redundancy, re-introduce useful inductive biases, and capture long-term temporal dynamics. In addition, we provide an overview of different training regimes and explore effective self-supervised learning strategies for video. Finally, we conduct a performance comparison on the most common benchmark for Video Transformers (i.e., action classification), finding them to outperform 3D ConvNets even with less computational complexity. |
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1 Nov. 2023 |
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HUPBA; no menciona |
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no |
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Admin @ si @ SJE2023 |
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3823 |
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Author |
Akshita Gupta; Sanath Narayan; Salman Khan; Fahad Shahbaz Khan; Ling Shao; Joost Van de Weijer |
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Title |
Generative Multi-Label Zero-Shot Learning |
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Journal Article |
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Year |
2023 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
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Volume |
45 |
Issue |
12 |
Pages |
14611-14624 |
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Keywords |
Generalized zero-shot learning; Multi-label classification; Zero-shot object detection; Feature synthesis |
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Abstract |
Multi-label zero-shot learning strives to classify images into multiple unseen categories for which no data is available during training. The test samples can additionally contain seen categories in the generalized variant. Existing approaches rely on learning either shared or label-specific attention from the seen classes. Nevertheless, computing reliable attention maps for unseen classes during inference in a multi-label setting is still a challenge. In contrast, state-of-the-art single-label generative adversarial network (GAN) based approaches learn to directly synthesize the class-specific visual features from the corresponding class attribute embeddings. However, synthesizing multi-label features from GANs is still unexplored in the context of zero-shot setting. When multiple objects occur jointly in a single image, a critical question is how to effectively fuse multi-class information. In this work, we introduce different fusion approaches at the attribute-level, feature-level and cross-level (across attribute and feature-levels) for synthesizing multi-label features from their corresponding multi-label class embeddings. To the best of our knowledge, our work is the first to tackle the problem of multi-label feature synthesis in the (generalized) zero-shot setting. Our cross-level fusion-based generative approach outperforms the state-of-the-art on three zero-shot benchmarks: NUS-WIDE, Open Images and MS COCO. Furthermore, we show the generalization capabilities of our fusion approach in the zero-shot detection task on MS COCO, achieving favorable performance against existing methods. |
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December 2023 |
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LAMP; PID2021-128178OB-I00 |
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no |
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Admin @ si @ |
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3853 |
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Author |
Shiqi Yang; Yaxing Wang; Joost Van de Weijer; Luis Herranz; Shangling Jui; Jian Yang |
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Title |
Trust Your Good Friends: Source-Free Domain Adaptation by Reciprocal Neighborhood Clustering |
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Journal Article |
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2023 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
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45 |
Issue |
12 |
Pages |
15883-15895 |
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Domain adaptation (DA) aims to alleviate the domain shift between source domain and target domain. Most DA methods require access to the source data, but often that is not possible (e.g., due to data privacy or intellectual property). In this paper, we address the challenging source-free domain adaptation (SFDA) problem, where the source pretrained model is adapted to the target domain in the absence of source data. Our method is based on the observation that target data, which might not align with the source domain classifier, still forms clear clusters. We capture this intrinsic structure by defining local affinity of the target data, and encourage label consistency among data with high local affinity. We observe that higher affinity should be assigned to reciprocal neighbors. To aggregate information with more context, we consider expanded neighborhoods with small affinity values. Furthermore, we consider the density around each target sample, which can alleviate the negative impact of potential outliers. In the experimental results we verify that the inherent structure of the target features is an important source of information for domain adaptation. We demonstrate that this local structure can be efficiently captured by considering the local neighbors, the reciprocal neighbors, and the expanded neighborhood. Finally, we achieve state-of-the-art performance on several 2D image and 3D point cloud recognition datasets. |
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LAMP; MACO |
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no |
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Admin @ si @ YWW2023 |
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3889 |
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Author |
F. Moreso; D. Seron; Jordi Vitria; J.M. Grinyo; F.M. Colome-Serra; N. Pares; J.R. Serra |
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Title |
Quantification of Interstitial Chronic Renal Damage by means of Texture Analysis. |
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1994 |
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Kidney International |
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46 |
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6 |
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1721-1727 |
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OR;MV |
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BCNPCL @ bcnpcl @ MSV1994 |
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113 |
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Albert Gordo; Florent Perronnin; Ernest Valveny |
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Large-scale document image retrieval and classification with runlength histograms and binary embeddings |
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2013 |
Publication |
Pattern Recognition |
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PR |
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46 |
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7 |
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1898-1905 |
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visual document descriptor; compression; large-scale; retrieval; classification |
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We present a new document image descriptor based on multi-scale runlength
histograms. This descriptor does not rely on layout analysis and can be
computed efficiently. We show how this descriptor can achieve state-of-theart
results on two very different public datasets in classification and retrieval
tasks. Moreover, we show how we can compress and binarize these descriptors
to make them suitable for large-scale applications. We can achieve state-ofthe-
art results in classification using binary descriptors of as few as 16 to 64
bits. |
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Elsevier |
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0031-3203 |
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DAG; 600.042; 600.045; 605.203 |
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Admin @ si @ GPV2013 |
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2306 |
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Albert Gordo; Alicia Fornes; Ernest Valveny |
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Writer identification in handwritten musical scores with bags of notes |
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2013 |
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Pattern Recognition |
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PR |
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46 |
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5 |
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1337-1345 |
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Writer Identification is an important task for the automatic processing of documents. However, the identification of the writer in graphical documents is still challenging. In this work, we adapt the Bag of Visual Words framework to the task of writer identification in handwritten musical scores. A vanilla implementation of this method already performs comparably to the state-of-the-art. Furthermore, we analyze the effect of two improvements of the representation: a Bhattacharyya embedding, which improves the results at virtually no extra cost, and a Fisher Vector representation that very significantly improves the results at the cost of a more complex and costly representation. Experimental evaluation shows results more than 20 points above the state-of-the-art in a new, challenging dataset. |
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0031-3203 |
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DAG |
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Admin @ si @ GFV2013 |
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2307 |
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Veronica Romero; Alicia Fornes; Nicolas Serrano; Joan Andreu Sanchez; A.H. Toselli; Volkmar Frinken; E. Vidal; Josep Llados |
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The ESPOSALLES database: An ancient marriage license corpus for off-line handwriting recognition |
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Journal Article |
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2013 |
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Pattern Recognition |
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PR |
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46 |
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6 |
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1658-1669 |
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Historical records of daily activities provide intriguing insights into the life of our ancestors, useful for demography studies and genealogical research. Automatic processing of historical documents, however, has mostly been focused on single works of literature and less on social records, which tend to have a distinct layout, structure, and vocabulary. Such information is usually collected by expert demographers that devote a lot of time to manually transcribe them. This paper presents a new database, compiled from a marriage license books collection, to support research in automatic handwriting recognition for historical documents containing social records. Marriage license books are documents that were used for centuries by ecclesiastical institutions to register marriage licenses. Books from this collection are handwritten and span nearly half a millennium until the beginning of the 20th century. In addition, a study is presented about the capability of state-of-the-art handwritten text recognition systems, when applied to the presented database. Baseline results are reported for reference in future studies. |
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Elsevier Science Inc. New York, NY, USA |
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0031-3203 |
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DAG; 600.045; 602.006; 605.203 |
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Admin @ si @ RFS2013 |
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2298 |
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Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
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A Genetic-based Subspace Analysis Method for Improving Error-Correcting Output Coding |
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Journal Article |
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2013 |
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Pattern Recognition |
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PR |
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46 |
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10 |
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2830-2839 |
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Error Correcting Output Codes; Evolutionary computation; Multiclass classification; Feature subspace; Ensemble classification |
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Two key factors affecting the performance of Error Correcting Output Codes (ECOC) in multiclass classification problems are the independence of binary classifiers and the problem-dependent coding design. In this paper, we propose an evolutionary algorithm-based approach to the design of an application-dependent codematrix in the ECOC framework. The central idea of this work is to design a three-dimensional codematrix, where the third dimension is the feature space of the problem domain. In order to do that, we consider the feature space in the design process of the codematrix with the aim of improving the independence and accuracy of binary classifiers. The proposed method takes advantage of some basic concepts of ensemble classification, such as diversity of classifiers, and also benefits from the evolutionary approach for optimizing the three-dimensional codematrix, taking into account the problem domain. We provide a set of experimental results using a set of benchmark datasets from the UCI Machine Learning Repository, as well as two real multiclass Computer Vision problems. Both sets of experiments are conducted using two different base learners: Neural Networks and Decision Trees. The results show that the proposed method increases the classification accuracy in comparison with the state-of-the-art ECOC coding techniques. |
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Elsevier |
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0031-3203 |
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HuPBA;MILAB |
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Admin @ si @ BGE2013a |
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2247 |
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Author |
Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados; Thierry Brouard |
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Title |
Fuzzy Multilevel Graph Embedding |
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Journal Article |
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Year |
2013 |
Publication |
Pattern Recognition |
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PR |
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46 |
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2 |
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551-565 |
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Keywords |
Pattern recognition; Graphics recognition; Graph clustering; Graph classification; Explicit graph embedding; Fuzzy logic |
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Abstract |
Structural pattern recognition approaches offer the most expressive, convenient, powerful but computational expensive representations of underlying relational information. To benefit from mature, less expensive and efficient state-of-the-art machine learning models of statistical pattern recognition they must be mapped to a low-dimensional vector space. Our method of explicit graph embedding bridges the gap between structural and statistical pattern recognition. We extract the topological, structural and attribute information from a graph and encode numeric details by fuzzy histograms and symbolic details by crisp histograms. The histograms are concatenated to achieve a simple and straightforward embedding of graph into a low-dimensional numeric feature vector. Experimentation on standard public graph datasets shows that our method outperforms the state-of-the-art methods of graph embedding for richly attributed graphs. |
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Elsevier |
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0031-3203 |
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DAG; 600.042; 600.045; 605.203 |
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Admin @ si @ LRL2013a |
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2270 |
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Author |
Anjan Dutta; Josep Llados; Umapada Pal |
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Title |
A symbol spotting approach in graphical documents by hashing serialized graphs |
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Journal Article |
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Year |
2013 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
46 |
Issue |
3 |
Pages |
752-768 |
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Keywords |
Symbol spotting; Graphics recognition; Graph matching; Graph serialization; Graph factorization; Graph paths; Hashing |
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Abstract |
In this paper we propose a symbol spotting technique in graphical documents. Graphs are used to represent the documents and a (sub)graph matching technique is used to detect the symbols in them. We propose a graph serialization to reduce the usual computational complexity of graph matching. Serialization of graphs is performed by computing acyclic graph paths between each pair of connected nodes. Graph paths are one-dimensional structures of graphs which are less expensive in terms of computation. At the same time they enable robust localization even in the presence of noise and distortion. Indexing in large graph databases involves a computational burden as well. We propose a graph factorization approach to tackle this problem. Factorization is intended to create a unified indexed structure over the database of graphical documents. Once graph paths are extracted, the entire database of graphical documents is indexed in hash tables by locality sensitive hashing (LSH) of shape descriptors of the paths. The hashing data structure aims to execute an approximate k-NN search in a sub-linear time. We have performed detailed experiments with various datasets of line drawings and compared our method with the state-of-the-art works. The results demonstrate the effectiveness and efficiency of our technique. |
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0031-3203 |
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DAG; 600.042; 600.045; 605.203; 601.152 |
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Admin @ si @ DLP2012 |
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2127 |
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Author |
Frederic Sampedro; Sergio Escalera; Anna Puig |
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Title |
Iterative Multiclass Multiscale Stacked Sequential Learning: definition and application to medical volume segmentation |
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Journal Article |
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Year |
2014 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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46 |
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Pages |
1-10 |
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Keywords |
Machine learning; Sequential learning; Multi-class problems; Contextual learning; Medical volume segmentation |
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Abstract |
In this work we present the iterative multi-class multi-scale stacked sequential learning framework (IMMSSL), a novel learning scheme that is particularly suited for medical volume segmentation applications. This model exploits the inherent voxel contextual information of the structures of interest in order to improve its segmentation performance results. Without any feature set or learning algorithm prior assumption, the proposed scheme directly seeks to learn the contextual properties of a region from the predicted classifications of previous classifiers within an iterative scheme. Performance results regarding segmentation accuracy in three two-class and multi-class medical volume datasets show a significant improvement with respect to state of the art alternatives. Due to its easiness of implementation and its independence of feature space and learning algorithm, the presented machine learning framework could be taken into consideration as a first choice in complex volume segmentation scenarios. |
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HuPBA;MILAB |
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Admin @ si @ SEP2014 |
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2550 |
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