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| Author | Suman Ghosh; Ernest Valveny | ||||
| Title | Query by String word spotting based on character bi-gram indexing | Type | Conference Article | ||
| Year | 2015 | Publication | 13th International Conference on Document Analysis and Recognition ICDAR2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 881-885 | ||
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Abstract  |
In this paper we propose a segmentation-free query by string word spotting method. Both the documents and query strings are encoded using a recently proposed word representa- tion that projects images and strings into a common atribute space based on a pyramidal histogram of characters(PHOC). These attribute models are learned using linear SVMs over the Fisher Vector representation of the images along with the PHOC labels of the corresponding strings. In order to search through the whole page, document regions are indexed per character bi- gram using a similar attribute representation. On top of that, we propose an integral image representation of the document using a simplified version of the attribute model for efficient computation. Finally we introduce a re-ranking step in order to boost retrieval performance. We show state-of-the-art results for segmentation-free query by string word spotting in single-writer and multi-writer standard datasets | ||||
| Address | Nancy; France; August 2015 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ GhV2015a | Serial | 2715 | ||
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| Author | Suman Ghosh; Ernest Valveny | ||||
| Title | A Sliding Window Framework for Word Spotting Based on Word Attributes | Type | Conference Article | ||
| Year | 2015 | Publication | Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 | Abbreviated Journal | |
| Volume | 9117 | Issue | Pages | 652-661 | |
| Keywords | Word spotting; Sliding window; Word attributes | ||||
| Abstract |
In this paper we propose a segmentation-free approach to word spotting. Word images are first encoded into feature vectors using Fisher Vector. Then, these feature vectors are used together with pyramidal histogram of characters labels (PHOC) to learn SVM-based attribute models. Documents are represented by these PHOC based word attributes. To efficiently compute the word attributes over a sliding window, we propose to use an integral image representation of the document using a simplified version of the attribute model. Finally we re-rank the top word candidates using the more discriminative full version of the word attributes. We show state-of-the-art results for segmentation-free query-by-example word spotting in single-writer and multi-writer standard datasets. | ||||
| Address | Santiago de Compostela; June 2015 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer International Publishing | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-319-19389-2 | Medium | |
| Area | Expedition | Conference | IbPRIA | ||
| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ GhV2015b | Serial | 2716 | ||
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| Author | Anguelos Nicolaou; Andrew Bagdanov; Marcus Liwicki; Dimosthenis Karatzas | ||||
| Title | Sparse Radial Sampling LBP for Writer Identification | Type | Conference Article | ||
| Year | 2015 | Publication | 13th International Conference on Document Analysis and Recognition ICDAR2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 716-720 | ||
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| Abstract |
In this paper we present the use of Sparse Radial Sampling Local Binary Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture classification. By adapting and extending the standard LBP operator to the particularities of text we get a generic text-as-texture classification scheme and apply it to writer identification. In experiments on CVL and ICDAR 2013 datasets, the proposed feature-set demonstrates State-Of-the-Art (SOA) performance. Among the SOA, the proposed method is the only one that is based on dense extraction of a single local feature descriptor. This makes it fast and applicable at the earliest stages in a DIA pipeline without the need for segmentation, binarization, or extraction of multiple features. | ||||
| Address | Nancy; France; August 2015 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ NBL2015 | Serial | 2692 | ||
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| Author | Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados | ||||
| Title | Automatic Verification of Properly Signed Multi-page Document Images | Type | Conference Article | ||
| Year | 2015 | Publication | Proceedings of the Eleventh International Symposium on Visual Computing | Abbreviated Journal | |
| Volume | 9475 | Issue | Pages | 327-336 | |
| Keywords | Document Image; Manual Inspection; Signature Verification; Rejection Criterion; Document Flow | ||||
| Abstract |
In this paper we present an industrial application for the automatic screening of incoming multi-page documents in a banking workflow aimed at determining whether these documents are properly signed or not. The proposed method is divided in three main steps. First individual pages are classified in order to identify the pages that should contain a signature. In a second step, we segment within those key pages the location where the signatures should appear. The last step checks whether the signatures are present or not. Our method is tested in a real large-scale environment and we report the results when checking two different types of real multi-page contracts, having in total more than 14,500 pages. | ||||
| Address | Las Vegas, Nevada, USA; December 2015 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | 9475 | Series Issue | Edition | ||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ISVC | ||
| Notes | DAG; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 3189 | ||
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| Author | Marçal Rusiñol; David Aldavert; Ricardo Toledo; Josep Llados | ||||
| Title | Efficient segmentation-free keyword spotting in historical document collections | Type | Journal Article | ||
| Year | 2015 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 48 | Issue | 2 | Pages | 545–555 |
| Keywords | Historical documents; Keyword spotting; Segmentation-free; Dense SIFT features; Latent semantic analysis; Product quantization | ||||
| Abstract |
In this paper we present an efficient segmentation-free word spotting method, applied in the context of historical document collections, that follows the query-by-example paradigm. We use a patch-based framework where local patches are described by a bag-of-visual-words model powered by SIFT descriptors. By projecting the patch descriptors to a topic space with the latent semantic analysis technique and compressing the descriptors with the product quantization method, we are able to efficiently index the document information both in terms of memory and time. The proposed method is evaluated using four different collections of historical documents achieving good performances on both handwritten and typewritten scenarios. The yielded performances outperform the recent state-of-the-art keyword spotting approaches. | ||||
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| Notes | DAG; ADAS; 600.076; 600.077; 600.061; 601.223; 602.006; 600.055 | Approved | no | ||
| Call Number | Admin @ si @ RAT2015a | Serial | 2544 | ||
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| Author | David Sanchez-Mendoza; David Masip; Agata Lapedriza | ||||
| Title | Emotion recognition from mid-level features | Type | Journal Article | ||
| Year | 2015 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 67 | Issue | Part 1 | Pages | 66–74 |
| Keywords | Facial expression; Emotion recognition; Action units; Computer vision | ||||
| Abstract |
In this paper we present a study on the use of Action Units as mid-level features for automatically recognizing basic and subtle emotions. We propose a representation model based on mid-level facial muscular movement features. We encode these movements dynamically using the Facial Action Coding System, and propose to use these intermediate features based on Action Units (AUs) to classify emotions. AUs activations are detected fusing a set of spatiotemporal geometric and appearance features. The algorithm is validated in two applications: (i) the recognition of 7 basic emotions using the publicly available Cohn-Kanade database, and (ii) the inference of subtle emotional cues in the Newscast database. In this second scenario, we consider emotions that are perceived cumulatively in longer periods of time. In particular, we Automatically classify whether video shoots from public News TV channels refer to Good or Bad news. To deal with the different video lengths we propose a Histogram of Action Units and compute it using a sliding window strategy on the frame sequences. Our approach achieves accuracies close to human perception. | ||||
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| Publisher | Elsevier B.V. | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0167-8655 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ SML2015 | Serial | 2746 | ||
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| Author | Nuria Cirera; Alicia Fornes; Josep Llados | ||||
| Title | Hidden Markov model topology optimization for handwriting recognition | Type | Conference Article | ||
| Year | 2015 | Publication | 13th International Conference on Document Analysis and Recognition ICDAR2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 626-630 | ||
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| Abstract |
In this paper we present a method to optimize the topology of linear left-to-right hidden Markov models. These models are very popular for sequential signals modeling on tasks such as handwriting recognition. Many topology definition methods select the number of states for a character model based
on character length. This can be a drawback when characters are shorter than the minimum allowed by the model, since they can not be properly trained nor recognized. The proposed method optimizes the number of states per model by automatically including convenient skip-state transitions and therefore it avoids the aforementioned problem.We discuss and compare our method with other character length-based methods such the Fixed, Bakis and Quantile methods. Our proposal performs well on off-line handwriting recognition task. |
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| Address | Nancy; France; August 2015 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.061; 602.006; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ CFL2015 | Serial | 2639 | ||
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| Author | G. Lisanti; I. Masi; Andrew Bagdanov; Alberto del Bimbo | ||||
| Title | Person Re-identification by Iterative Re-weighted Sparse Ranking | Type | Journal Article | ||
| Year | 2015 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 37 | Issue | 8 | Pages | 1629 - 1642 |
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| Abstract |
In this paper we introduce a method for person re-identification based on discriminative, sparse basis expansions of targets in terms of a labeled gallery of known individuals. We propose an iterative extension to sparse discriminative classifiers capable of ranking many candidate targets. The approach makes use of soft- and hard- re-weighting to redistribute energy among the most relevant contributing elements and to ensure that the best candidates are ranked at each iteration. Our approach also leverages a novel visual descriptor which we show to be discriminative while remaining robust to pose and illumination variations. An extensive comparative evaluation is given demonstrating that our approach achieves state-of-the-art performance on single- and multi-shot person re-identification scenarios on the VIPeR, i-LIDS, ETHZ, and CAVIAR4REID datasets. The combination of our descriptor and iterative sparse basis expansion improves state-of-the-art rank-1 performance by six percentage points on VIPeR and by 20 on CAVIAR4REID compared to other methods with a single gallery image per person. With multiple gallery and probe images per person our approach improves by 17 percentage points the state-of-the-art on i-LIDS and by 72 on CAVIAR4REID at rank-1. The approach is also quite efficient, capable of single-shot person re-identification over galleries containing hundreds of individuals at about 30 re-identifications per second. | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0162-8828 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | LAMP; 601.240; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ LMB2015 | Serial | 2557 | ||
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| Author | Francisco Alvaro; Francisco Cruz; Joan Andreu Sanchez; Oriol Ramos Terrades; Jose Miguel Benedi | ||||
| Title | Structure Detection and Segmentation of Documents Using 2D Stochastic Context-Free Grammars | Type | Journal Article | ||
| Year | 2015 | Publication | Neurocomputing | Abbreviated Journal | NEUCOM |
| Volume | 150 | Issue | A | Pages | 147-154 |
| Keywords | document image analysis; stochastic context-free grammars; text classi cation features | ||||
| Abstract |
In this paper we de ne a bidimensional extension of Stochastic Context-Free Grammars for structure detection and segmentation of images of documents.
Two sets of text classi cation features are used to perform an initial classi cation of each zone of the page. Then, the document segmentation is obtained as the most likely hypothesis according to a stochastic grammar. We used a dataset of historical marriage license books to validate this approach. We also tested several inference algorithms for Probabilistic Graphical Models and the results showed that the proposed grammatical model outperformed the other methods. Furthermore, grammars also provide the document structure along with its segmentation. |
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| Notes | DAG; 601.158; 600.077; 600.061 | Approved | no | ||
| Call Number | Admin @ si @ ACS2015 | Serial | 2531 | ||
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| Author | Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier; Josep Llados | ||||
| Title | A Comparative Study of Local Detectors and Descriptors for Mobile Document Classification | Type | Conference Article | ||
| Year | 2015 | Publication | 13th International Conference on Document Analysis and Recognition ICDAR2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 596-600 | ||
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| Abstract |
In this paper we conduct a comparative study of local key-point detectors and local descriptors for the specific task of mobile document classification. A classification architecture based on direct matching of local descriptors is used as baseline for the comparative study. A set of four different key-point
detectors and four different local descriptors are tested in all the possible combinations. The experiments are conducted in a database consisting of 30 model documents acquired on 6 different backgrounds, totaling more than 36.000 test images. |
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| Address | Nancy; France; August 2015 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.084; 600.61; 601.223; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ RCO2015 | Serial | 2684 | ||
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| Author | Joost Van de Weijer; Fahad Shahbaz Khan | ||||
| Title | An Overview of Color Name Applications in Computer Vision | Type | Conference Article | ||
| Year | 2015 | Publication | Computational Color Imaging Workshop | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | color features; color names; object recognition | ||||
| Abstract |
In this article we provide an overview of color name applications in computer vision. Color names are linguistic labels which humans use to communicate color. Computational color naming learns a mapping from pixels values to color names. In recent years color names have been applied to a wide variety of computer vision applications, including image classification, object recognition, texture classification, visual tracking and action recognition. Here we provide an overview of these results which show that in general color names outperform photometric invariants as a color representation. | ||||
| Address | Saint Etienne; France; March 2015 | ||||
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| Area | Expedition | Conference | CCIW | ||
| Notes | LAMP; 600.079; 600.068 | Approved | no | ||
| Call Number | Admin @ si @ WeK2015 | Serial | 2586 | ||
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| Author | Mikhail Mozerov; Joost Van de Weijer | ||||
| Title | Accurate stereo matching by two step global optimization | Type | Journal Article | ||
| Year | 2015 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 24 | Issue | 3 | Pages | 1153-1163 |
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| Abstract |
In stereo matching cost filtering methods and energy minimization algorithms are considered as two different techniques. Due to their global extend energy minimization methods obtain good stereo matching results. However, they tend to fail in occluded regions, in which cost filtering approaches obtain better results. In this paper we intend to combine both approaches with the aim to improve overall stereo matching results. We show that a global optimization with a fully connected model can be solved by cost fil tering methods. Based on this observation we propose to perform stereo matching as a two-step energy minimization algorithm. We consider two MRF models: a fully connected model defined on the complete set of pixels in an image and a conventional locally connected model. We solve the energy minimization problem for the fully connected model, after which the marginal function of the solution is used as the unary potential in the locally connected MRF model. Experiments on the Middlebury stereo datasets show that the proposed method achieves state-of-the-arts results. | ||||
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| ISSN | 1057-7149 | ISBN | Medium | ||
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| Notes | ISE; LAMP; 600.079; 600.078 | Approved | no | ||
| Call Number | Admin @ si @ MoW2015a | Serial | 2568 | ||
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| Author | Firat Ismailoglu; Ida G. Sprinkhuizen-Kuyper; Evgueni Smirnov; Sergio Escalera; Ralf Peeters | ||||
| Title | Fractional Programming Weighted Decoding for Error-Correcting Output Codes | Type | Conference Article | ||
| Year | 2015 | Publication | Multiple Classifier Systems, Proceedings of 12th International Workshop , MCS 2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 38-50 | ||
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| Abstract |
In order to increase the classification performance obtained using Error-Correcting Output Codes designs (ECOC), introducing weights in the decoding phase of the ECOC has attracted a lot of interest. In this work, we present a method for ECOC designs that focuses on increasing hypothesis margin on the data samples given a base classifier. While achieving this, we implicitly reward the base classifiers with high performance, whereas punish those with low performance. The resulting objective function is of the fractional programming type and we deal with this problem through the Dinkelbach’s Algorithm. The conducted tests over well known UCI datasets show that the presented method is superior to the unweighted decoding and that it outperforms the results of the state-of-the-art weighted decoding methods in most of the performed experiments. | ||||
| Address | Gunzburg; Germany; June 2015 | ||||
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| Publisher | Springer International Publishing | Place of Publication | Editor | ||
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| ISSN | ISBN | 978-3-319-20247-1 | Medium | ||
| Area | Expedition | Conference | MCS | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ ISS2015 | Serial | 2601 | ||
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| Author | Bogdan Raducanu; Alireza Bosaghzadeh; Fadi Dornaika | ||||
| Title | Multi-observation Face Recognition in Videos based on Label Propagation | Type | Conference Article | ||
| Year | 2015 | Publication | 6th Workshop on Analysis and Modeling of Faces and Gestures AMFG2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 10-17 | ||
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| Abstract |
In order to deal with the huge amount of content generated by social media, especially for indexing and retrieval purposes, the focus shifted from single object recognition to multi-observation object recognition. Of particular interest is the problem of face recognition (used as primary cue for persons’ identity assessment), since it is highly required by popular social media search engines like Facebook and Youtube. Recently, several approaches for graph-based label propagation were proposed. However, the associated graphs were constructed in an ad-hoc manner (e.g., using the KNN graph) that cannot cope properly with the rapid and frequent changes in data appearance, a phenomenon intrinsically related with video sequences. In this paper, we
propose a novel approach for efficient and adaptive graph construction, based on a two-phase scheme: (i) the first phase is used to adaptively find the neighbors of a sample and also to find the adequate weights for the minimization function of the second phase; (ii) in the second phase, the selected neighbors along with their corresponding weights are used to locally and collaboratively estimate the sparse affinity matrix weights. Experimental results performed on Honda Video Database (HVDB) and a subset of video sequences extracted from the popular TV-series ’Friends’ show a distinct advantage of the proposed method over the existing standard graph construction methods. |
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| Address | Boston; USA; June 2015 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | LAMP; 600.068; 600.072; | Approved | no | ||
| Call Number | Admin @ si @ RBD2015 | Serial | 2627 | ||
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| Author | Miguel Oliveira; L. Seabra Lopes; G. Hyun Lim; S. Hamidreza Kasaei; Angel Sappa; A. Tom | ||||
| Title | Concurrent Learning of Visual Codebooks and Object Categories in Openended Domains | Type | Conference Article | ||
| Year | 2015 | Publication | International Conference on Intelligent Robots and Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 2488 - 2495 | ||
| Keywords | Visual Learning; Computer Vision; Autonomous Agents | ||||
| Abstract |
In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using offline constructed codebooks. | ||||
| Address | Hamburg; Germany; October 2015 | ||||
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| Area | Expedition | Conference | IROS | ||
| Notes | ADAS; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ OSL2015 | Serial | 2664 | ||
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