|
Records |
Links |
|
Author |
Marçal Rusiñol; David Aldavert; Ricardo Toledo; Josep Llados |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
|
|
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Efficient segmentation-free keyword spotting in historical document collections |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Historical documents; Keyword spotting; Segmentation-free; Dense SIFT features; Latent semantic analysis; Product quantization |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In this paper we present an efficient segmentation-free word spotting method, applied in the context of historical document collections, that follows the query-by-example paradigm.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 The proposed method is evaluated using four different collections of historical documents achieving good performances on both handwritten and typewritten scenarios.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. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher ![sorted by Publisher field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; ADAS; 600.076; 600.077; 600.061; 601.223; 602.006; 600.055 |
Approved |
no |
|
|
Call Number |
Admin @ si @ RAT2015a |
Serial |
2544 |
|
Permanent link to this record |
|
|
|
|
Author |
Katerine Diaz; Francesc J. Ferri; W. Diaz |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
|
|
Title |
Incremental Generalized Discriminative Common Vectors for Image Classification |
Type |
Journal Article |
|
Year |
2015 |
Publication |
IEEE Transactions on Neural Networks and Learning Systems |
Abbreviated Journal |
TNNLS |
|
|
Volume |
26 |
Issue |
8 |
Pages |
1761 - 1775 |
|
|
Keywords |
|
|
|
Abstract |
Subspace-based methods have become popular due to their ability to appropriately represent complex data in such a way that both dimensionality is reduced and discriminativeness is enhanced. Several recent works have concentrated on the discriminative common vector (DCV) method and other closely related algorithms also based on the concept of null space. In this paper, we present a generalized incremental formulation of the DCV methods, which allows the update of a given model by considering the addition of new examples even from unseen classes. Having efficient incremental formulations of well-behaved batch algorithms allows us to conveniently adapt previously trained classifiers without the need of recomputing them from scratch. The proposed generalized incremental method has been empirically validated in different case studies from different application domains (faces, objects, and handwritten digits) considering several different scenarios in which new data are continuously added at different rates starting from an initial model. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher ![sorted by Publisher field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
2162-237X |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS; 600.076 |
Approved |
no |
|
|
Call Number |
Admin @ si @ DFD2015 |
Serial |
2547 |
|
Permanent link to this record |
|
|
|
|
Author |
Miguel Oliveira; Angel Sappa; Victor Santos |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
|
|
Title |
A probabilistic approach for color correction in image mosaicking applications |
Type |
Journal Article |
|
Year |
2015 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
|
|
Volume |
14 |
Issue |
2 |
Pages |
508 - 523 |
|
|
Keywords |
Color correction; image mosaicking; color transfer; color palette mapping functions |
|
|
Abstract |
Image mosaicking applications require both geometrical and photometrical registrations between the images that compose the mosaic. This paper proposes a probabilistic color correction algorithm for correcting the photometrical disparities. First, the image to be color corrected is segmented into several regions using mean shift. Then, connected regions are extracted using a region fusion algorithm. Local joint image histograms of each region are modeled as collections of truncated Gaussians using a maximum likelihood estimation procedure. Then, local color palette mapping functions are computed using these sets of Gaussians. The color correction is performed by applying those functions to all the regions of the image. An extensive comparison with ten other state of the art color correction algorithms is presented, using two different image pair data sets. Results show that the proposed approach obtains the best average scores in both data sets and evaluation metrics and is also the most robust to failures. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher ![sorted by Publisher field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
1057-7149 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS; 600.076 |
Approved |
no |
|
|
Call Number |
Admin @ si @ OSS2015b |
Serial |
2554 |
|
Permanent link to this record |
|
|
|
|
Author |
Francisco Blanco; Felipe Lumbreras; Joan Serrat; Roswitha Siener; Silvia Serranti; Giuseppe Bonifazi; Montserrat Lopez Mesas; Manuel Valiente |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
|
|
Title |
Taking advantage of Hyperspectral Imaging classification of urinary stones against conventional IR Spectroscopy |
Type |
Journal Article |
|
Year |
2014 |
Publication |
Journal of Biomedical Optics |
Abbreviated Journal |
JBiO |
|
|
Volume |
19 |
Issue |
12 |
Pages |
126004-1 - 126004-9 |
|
|
Keywords |
|
|
|
Abstract |
The analysis of urinary stones is mandatory for the best management of the disease after the stone passage in order to prevent further stone episodes. Thus the use of an appropriate methodology for an individualized stone analysis becomes a key factor for giving the patient the most suitable treatment. A recently developed hyperspectral imaging methodology, based on pixel-to-pixel analysis of near-infrared spectral images, is compared to the reference technique in stone analysis, infrared (IR) spectroscopy. The developed classification model yields >90% correct classification rate when compared to IR and is able to precisely locate stone components within the structure of the stone with a 15 µm resolution. Due to the little sample pretreatment, low analysis time, good performance of the model, and the automation of the measurements, they become analyst independent; this methodology can be considered to become a routine analysis for clinical laboratories. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher ![sorted by Publisher field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS; 600.076 |
Approved |
no |
|
|
Call Number |
Admin @ si @ BLS2014 |
Serial |
2563 |
|
Permanent link to this record |
|
|
|
|
Author |
Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Michael Felsberg; J.Laaksonen |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
|
|
Title |
Compact color texture description for texture classification |
Type |
Journal Article |
|
Year |
2015 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
|
|
Volume |
51 |
Issue |
|
Pages |
16-22 |
|
|
Keywords |
|
|
|
Abstract |
Describing textures is a challenging problem in computer vision and pattern recognition. The classification problem involves assigning a category label to the texture class it belongs to. Several factors such as variations in scale, illumination and viewpoint make the problem of texture description extremely challenging. A variety of histogram based texture representations exists in literature.
However, combining multiple texture descriptors and assessing their complementarity is still an open research problem. In this paper, we first show that combining multiple local texture descriptors significantly improves the recognition performance compared to using a single best method alone. This
gain in performance is achieved at the cost of high-dimensional final image representation. To counter this problem, we propose to use an information-theoretic compression technique to obtain a compact texture description without any significant loss in accuracy. In addition, we perform a comprehensive
evaluation of pure color descriptors, popular in object recognition, for the problem of texture classification. Experiments are performed on four challenging texture datasets namely, KTH-TIPS-2a, KTH-TIPS-2b, FMD and Texture-10. The experiments clearly demonstrate that our proposed compact multi-texture approach outperforms the single best texture method alone. In all cases, discriminative color names outperforms other color features for texture classification. Finally, we show that combining discriminative color names with compact texture representation outperforms state-of-the-art methods by 7:8%, 4:3% and 5:0% on KTH-TIPS-2a, KTH-TIPS-2b and Texture-10 datasets respectively. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher ![sorted by Publisher field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
LAMP; 600.068; 600.079;ADAS |
Approved |
no |
|
|
Call Number |
Admin @ si @ KRW2015a |
Serial |
2587 |
|
Permanent link to this record |