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Author |
Yunchao Gong; Svetlana Lazebnik; Albert Gordo; Florent Perronnin |
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Title |
Iterative quantization: A procrustean approach to learning binary codes for Large-Scale Image Retrieval |
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2012 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
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35 |
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12 |
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2916-2929 |
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This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections. We formulate this problem in terms of finding a rotation of zero-centered data so as to minimize the quantization error of mapping this data to the vertices of a zero-centered binary hypercube, and propose a simple and efficient alternating minimization algorithm to accomplish this task. This algorithm, dubbed iterative quantization (ITQ), has connections to multi-class spectral clustering and to the orthogonal Procrustes problem, and it can be used both with unsupervised data embeddings such as PCA and supervised embeddings such as canonical correlation analysis (CCA). The resulting binary codes significantly outperform several other state-of-the-art methods. We also show that further performance improvements can result from transforming the data with a nonlinear kernel mapping prior to PCA or CCA. Finally, we demonstrate an application of ITQ to learning binary attributes or “classemes” on the ImageNet dataset. |
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0162-8828 |
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978-1-4577-0394-2 |
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DAG |
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Admin @ si @ GLG 2012b |
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2008 |
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Author |
Oriol Ramos Terrades; Ernest Valveny; Salvatore Tabbone |
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Title |
Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework |
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2009 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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31 |
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9 |
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1630–1644 |
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The combination of the output of classifiers has been one of the strategies used to improve classification rates in general purpose classification systems. Some of the most common approaches can be explained using the Bayes' formula. In this paper, we tackle the problem of the combination of classifiers using a non-Bayesian probabilistic framework. This approach permits us to derive two linear combination rules that minimize misclassification rates under some constraints on the distribution of classifiers. In order to show the validity of this approach we have compared it with other popular combination rules from a theoretical viewpoint using a synthetic data set, and experimentally using two standard databases: the MNIST handwritten digit database and the GREC symbol database. Results on the synthetic data set show the validity of the theoretical approach. Indeed, results on real data show that the proposed methods outperform other common combination schemes. |
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0162-8828 |
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DAG @ dag @ RVT2009 |
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1220 |
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Author |
Anjan Dutta; Hichem Sahbi |
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Title |
Stochastic Graphlet Embedding |
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Journal Article |
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2018 |
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IEEE Transactions on Neural Networks and Learning Systems |
Abbreviated Journal |
TNNLS |
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1-14 |
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Stochastic graphlets; Graph embedding; Graph classification; Graph hashing; Betweenness centrality |
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Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semi-structured data as graphs where nodes correspond to primitives (parts, interest points, segments,
etc.) and edges characterize the relationships between these primitives. However, these non-vectorial graph data cannot be straightforwardly plugged into off-the-shelf machine learning algorithms without a preliminary step of – explicit/implicit –graph vectorization and embedding. This embedding process
should be resilient to intra-class graph variations while being highly discriminant. In this paper, we propose a novel high-order stochastic graphlet embedding (SGE) that maps graphs into vector spaces. Our main contribution includes a new stochastic search procedure that efficiently parses a given graph and extracts/samples unlimitedly high-order graphlets. We consider
these graphlets, with increasing orders, to model local primitives as well as their increasingly complex interactions. In order to build our graph representation, we measure the distribution of these graphlets into a given graph, using particular hash functions that efficiently assign sampled graphlets into isomorphic sets with a very low probability of collision. When
combined with maximum margin classifiers, these graphlet-based representations have positive impact on the performance of pattern comparison and recognition as corroborated through extensive experiments using standard benchmark databases. |
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DAG; 602.167; 602.168; 600.097; 600.121 |
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Call Number |
Admin @ si @ DuS2018 |
Serial |
3225 |
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Author |
Carlos Boned Riera; Oriol Ramos Terrades |
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Title |
Discriminative Neural Variational Model for Unbalanced Classification Tasks in Knowledge Graph |
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Conference Article |
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Year |
2022 |
Publication |
26th International Conference on Pattern Recognition |
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2186-2191 |
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Measurement; Couplings; Semantics; Ear; Benchmark testing; Data models; Pattern recognition |
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Nowadays the paradigm of link discovery problems has shown significant improvements on Knowledge Graphs. However, method performances are harmed by the unbalanced nature of this classification problem, since many methods are easily biased to not find proper links. In this paper we present a discriminative neural variational auto-encoder model, called DNVAE from now on, in which we have introduced latent variables to serve as embedding vectors. As a result, the learnt generative model approximate better the underlying distribution and, at the same time, it better differentiate the type of relations in the knowledge graph. We have evaluated this approach on benchmark knowledge graph and Census records. Results in this last data set are quite impressive since we reach the highest possible score in the evaluation metrics. However, further experiments are still needed to deeper evaluate the performance of the method in more challenging tasks. |
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Montreal; Quebec; Canada; August 2022 |
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DAG; 600.121; 600.162 |
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Admin @ si @ BoR2022 |
Serial |
3741 |
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Author |
Mohamed Ali Souibgui; Sanket Biswas; Sana Khamekhem Jemni; Yousri Kessentini; Alicia Fornes; Josep Llados; Umapada Pal |
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Title |
DocEnTr: An End-to-End Document Image Enhancement Transformer |
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Conference Article |
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Year |
2022 |
Publication |
26th International Conference on Pattern Recognition |
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1699-1705 |
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Degradation; Head; Optical character recognition; Self-supervised learning; Benchmark testing; Transformers; Magnetic heads |
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Document images can be affected by many degradation scenarios, which cause recognition and processing difficulties. In this age of digitization, it is important to denoise them for proper usage. To address this challenge, we present a new encoder-decoder architecture based on vision transformers to enhance both machine-printed and handwritten document images, in an end-to-end fashion. The encoder operates directly on the pixel patches with their positional information without the use of any convolutional layers, while the decoder reconstructs a clean image from the encoded patches. Conducted experiments show a superiority of the proposed model compared to the state-of the-art methods on several DIBCO benchmarks. Code and models will be publicly available at: https://github.com/dali92002/DocEnTR |
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August 21-25, 2022 , Montréal Québec |
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DAG; 600.121; 600.162; 602.230; 600.140 |
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Call Number |
Admin @ si @ SBJ2022 |
Serial |
3730 |
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Author |
Mohamed Ali Souibgui; Alicia Fornes; Y.Kessentini; C.Tudor |
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Title |
A Few-shot Learning Approach for Historical Encoded Manuscript Recognition |
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Conference Article |
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Year |
2021 |
Publication |
25th International Conference on Pattern Recognition |
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5413-5420 |
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Encoded (or ciphered) manuscripts are a special type of historical documents that contain encrypted text. The automatic recognition of this kind of documents is challenging because: 1) the cipher alphabet changes from one document to another, 2) there is a lack of annotated corpus for training and 3) touching symbols make the symbol segmentation difficult and complex. To overcome these difficulties, we propose a novel method for handwritten ciphers recognition based on few-shot object detection. Our method first detects all symbols of a given alphabet in a line image, and then a decoding step maps the symbol similarity scores to the final sequence of transcribed symbols. By training on synthetic data, we show that the proposed architecture is able to recognize handwritten ciphers with unseen alphabets. In addition, if few labeled pages with the same alphabet are used for fine tuning, our method surpasses existing unsupervised and supervised HTR methods for ciphers recognition. |
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Virtual; January 2021 |
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DAG; 600.121; 600.140 |
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Admin @ si @ SFK2021 |
Serial |
3449 |
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Author |
Sounak Dey; Anjan Dutta; Suman Ghosh; Ernest Valveny; Josep Llados; Umapada Pal |
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Title |
Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch |
Type |
Conference Article |
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Year |
2018 |
Publication |
24th International Conference on Pattern Recognition |
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916 - 921 |
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In this work we introduce a cross modal image retrieval system that allows both text and sketch as input modalities for the query. A cross-modal deep network architecture is formulated to jointly model the sketch and text input modalities as well as the the image output modality, learning a common embedding between text and images and between sketches and images. In addition, an attention model is used to selectively focus the attention on the different objects of the image, allowing for retrieval with multiple objects in the query. Experiments show that the proposed method performs the best in both single and multiple object image retrieval in standard datasets. |
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Beijing; China; August 2018 |
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DAG; 602.167; 602.168; 600.097; 600.084; 600.121; 600.129 |
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Admin @ si @ DDG2018b |
Serial |
3152 |
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Author |
Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes |
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Title |
Learning Graph Distances with Message Passing Neural Networks |
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2018 |
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24th International Conference on Pattern Recognition |
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2239-2244 |
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★Best Paper Award★ |
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Abstract |
Graph representations have been widely used in pattern recognition thanks to their powerful representation formalism and rich theoretical background. A number of error-tolerant graph matching algorithms such as graph edit distance have been proposed for computing a distance between two labelled graphs. However, they typically suffer from a high
computational complexity, which makes it difficult to apply
these matching algorithms in a real scenario. In this paper, we propose an efficient graph distance based on the emerging field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure and learns a metric with a siamese network approach. The performance of the proposed graph distance is validated in two application cases, graph classification and graph retrieval of handwritten words, and shows a promising performance when compared with
(approximate) graph edit distance benchmarks. |
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Beijing; China; August 2018 |
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DAG; 600.097; 603.057; 601.302; 600.121 |
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Admin @ si @ RFL2018 |
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3168 |
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Author |
Francisco Cruz; Oriol Ramos Terrades |
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Title |
EM-Based Layout Analysis Method for Structured Documents |
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2014 |
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22nd International Conference on Pattern Recognition |
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315-320 |
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In this paper we present a method to perform layout analysis in structured documents. We proposed an EM-based algorithm to fit a set of Gaussian mixtures to the different regions according to the logical distribution along the page. After the convergence, we estimate the final shape of the regions according
to the parameters computed for each component of the mixture. We evaluated our method in the task of record detection in a collection of historical structured documents and performed a comparison with other previous works in this task. |
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1051-4651 |
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DAG; 602.006; 600.061; 600.077 |
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Admin @ si @ CrR2014 |
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2530 |
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Author |
Lluis Gomez; Dimosthenis Karatzas |
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MSER-based Real-Time Text Detection and Tracking |
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2014 |
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22nd International Conference on Pattern Recognition |
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3110 - 3115 |
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We present a hybrid algorithm for detection and tracking of text in natural scenes that goes beyond the fulldetection approaches in terms of time performance optimization.
A state-of-the-art scene text detection module based on Maximally Stable Extremal Regions (MSER) is used to detect text asynchronously, while on a separate thread detected text objects are tracked by MSER propagation. The cooperation of these two modules yields real time video processing at high frame rates even on low-resource devices. |
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Stockholm; August 2014 |
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DAG; 600.056; 601.158; 601.197; 600.077 |
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Admin @ si @ GoK2014a |
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2492 |
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