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Author |
Marçal Rusiñol; Lluis Pere de las Heras; Oriol Ramos Terrades |
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Title |
Flowchart Recognition for Non-Textual Information Retrieval in Patent Search |
Type |
Journal Article |
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Year |
2014 |
Publication |
Information Retrieval |
Abbreviated Journal |
IR |
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Volume |
17 |
Issue |
5-6 |
Pages |
545-562 |
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Keywords |
Flowchart recognition; Patent documents; Text/graphics separation; Raster-to-vector conversion; Symbol recognition |
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Abstract |
Relatively little research has been done on the topic of patent image retrieval and in general in most of the approaches the retrieval is performed in terms of a similarity measure between the query image and the images in the corpus. However, systems aimed at overcoming the semantic gap between the visual description of patent images and their conveyed concepts would be very helpful for patent professionals. In this paper we present a flowchart recognition method aimed at achieving a structured representation of flowchart images that can be further queried semantically. The proposed method was submitted to the CLEF-IP 2012 flowchart recognition task. We report the obtained results on this dataset. |
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1386-4564 |
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DAG; 600.077 |
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no |
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Admin @ si @ RHR2013 |
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2342 |
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Author |
Palaiahnakote Shivakumara; Anjan Dutta; Chew Lim Tan; Umapada Pal |
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Title |
Multi-oriented scene text detection in video based on wavelet and angle projection boundary growing |
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Journal Article |
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Year |
2014 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
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Volume |
72 |
Issue |
1 |
Pages |
515-539 |
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In this paper, we address two complex issues: 1) Text frame classification and 2) Multi-oriented text detection in video text frame. We first divide a video frame into 16 blocks and propose a combination of wavelet and median-moments with k-means clustering at the block level to identify probable text blocks. For each probable text block, the method applies the same combination of feature with k-means clustering over a sliding window running through the blocks to identify potential text candidates. We introduce a new idea of symmetry on text candidates in each block based on the observation that pixel distribution in text exhibits a symmetric pattern. The method integrates all blocks containing text candidates in the frame and then all text candidates are mapped on to a Sobel edge map of the original frame to obtain text representatives. To tackle the multi-orientation problem, we present a new method called Angle Projection Boundary Growing (APBG) which is an iterative algorithm and works based on a nearest neighbor concept. APBG is then applied on the text representatives to fix the bounding box for multi-oriented text lines in the video frame. Directional information is used to eliminate false positives. Experimental results on a variety of datasets such as non-horizontal, horizontal, publicly available data (Hua’s data) and ICDAR-03 competition data (camera images) show that the proposed method outperforms existing methods proposed for video and the state of the art methods for scene text as well. |
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Springer US |
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1380-7501 |
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DAG; 600.077 |
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no |
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Admin @ si @ SDT2014 |
Serial |
2357 |
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Author |
Cesar Isaza; Joaquin Salas; Bogdan Raducanu |
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Title |
Rendering ground truth data sets to detect shadows cast by static objects in outdoors |
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Journal Article |
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Year |
2014 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
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Volume |
70 |
Issue |
1 |
Pages |
557-571 |
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Synthetic ground truth data set; Sun position; Shadow detection; Static objects shadow detection |
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In our work, we are particularly interested in studying the shadows cast by static objects in outdoor environments, during daytime. To assess the accuracy of a shadow detection algorithm, we need ground truth information. The collection of such information is a very tedious task because it is a process that requires manual annotation. To overcome this severe limitation, we propose in this paper a methodology to automatically render ground truth using a virtual environment. To increase the degree of realism and usefulness of the simulated environment, we incorporate in the scenario the precise longitude, latitude and elevation of the actual location of the object, as well as the sun’s position for a given time and day. To evaluate our method, we consider a qualitative and a quantitative comparison. In the quantitative one, we analyze the shadow cast by a real object in a particular geographical location and its corresponding rendered model. To evaluate qualitatively the methodology, we use some ground truth images obtained both manually and automatically. |
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Springer US |
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1380-7501 |
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LAMP; |
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no |
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Admin @ si @ ISR2014 |
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2229 |
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Author |
Shida Beigpour; Christian Riess; Joost Van de Weijer; Elli Angelopoulou |
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Title |
Multi-Illuminant Estimation with Conditional Random Fields |
Type |
Journal Article |
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Year |
2014 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
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Volume |
23 |
Issue |
1 |
Pages |
83-95 |
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Keywords |
color constancy; CRF; multi-illuminant |
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Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel data set of two-dominant-illuminant images comprised of laboratory, indoor, and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation approach. |
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1057-7149 |
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Notes |
CIC; LAMP; 600.074; 600.079 |
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no |
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Call Number |
Admin @ si @ BRW2014 |
Serial |
2451 |
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Author |
Fahad Shahbaz Khan; Joost Van de Weijer; Muhammad Anwer Rao; Michael Felsberg; Carlo Gatta |
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Title |
Semantic Pyramids for Gender and Action Recognition |
Type |
Journal Article |
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Year |
2014 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
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Volume |
23 |
Issue |
8 |
Pages |
3633-3645 |
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Abstract |
Person description is a challenging problem in computer vision. We investigated two major aspects of person description: 1) gender and 2) action recognition in still images. Most state-of-the-art approaches for gender and action recognition rely on the description of a single body part, such as face or full-body. However, relying on a single body part is suboptimal due to significant variations in scale, viewpoint, and pose in real-world images. This paper proposes a semantic pyramid approach for pose normalization. Our approach is fully automatic and based on combining information from full-body, upper-body, and face regions for gender and action recognition in still images. The proposed approach does not require any annotations for upper-body and face of a person. Instead, we rely on pretrained state-of-the-art upper-body and face detectors to automatically extract semantic information of a person. Given multiple bounding boxes from each body part detector, we then propose a simple method to select the best candidate bounding box, which is used for feature extraction. Finally, the extracted features from the full-body, upper-body, and face regions are combined into a single representation for classification. To validate the proposed approach for gender recognition, experiments are performed on three large data sets namely: 1) human attribute; 2) head-shoulder; and 3) proxemics. For action recognition, we perform experiments on four data sets most used for benchmarking action recognition in still images: 1) Sports; 2) Willow; 3) PASCAL VOC 2010; and 4) Stanford-40. Our experiments clearly demonstrate that the proposed approach, despite its simplicity, outperforms state-of-the-art methods for gender and action recognition. |
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1057-7149 |
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CIC; LAMP; 601.160; 600.074; 600.079;MILAB |
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no |
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Call Number |
Admin @ si @ KWR2014 |
Serial |
2507 |
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Author |
Noha Elfiky; Theo Gevers; Arjan Gijsenij; Jordi Gonzalez |
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Title |
Color Constancy using 3D Scene Geometry derived from a Single Image |
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Journal Article |
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Year |
2014 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
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Volume |
23 |
Issue |
9 |
Pages |
3855-3868 |
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Abstract |
The aim of color constancy is to remove the effect of the color of the light source. As color constancy is inherently an ill-posed problem, most of the existing color constancy algorithms are based on specific imaging assumptions (e.g. grey-world and white patch assumption).
In this paper, 3D geometry models are used to determine which color constancy method to use for the different geometrical regions (depth/layer) found
in images. The aim is to classify images into stages (rough 3D geometry models). According to stage models; images are divided into stage regions using hard and soft segmentation. After that, the best color constancy methods is selected for each geometry depth. To this end, we propose a method to combine color constancy algorithms by investigating the relation between depth, local image statistics and color constancy. Image statistics are then exploited per depth to select the proper color constancy method. Our approach opens the possibility to estimate multiple illuminations by distinguishing
nearby light source from distant illuminations. Experiments on state-of-the-art data sets show that the proposed algorithm outperforms state-of-the-art
single color constancy algorithms with an improvement of almost 50% of median angular error. When using a perfect classifier (i.e, all of the test images are correctly classified into stages); the performance of the proposed method achieves an improvement of 52% of the median angular error compared to the best-performing single color constancy algorithm. |
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1057-7149 |
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Notes |
ISE; 600.078 |
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no |
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Call Number |
Admin @ si @ EGG2014 |
Serial |
2528 |
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Author |
Jiaolong Xu; Sebastian Ramos;David Vazquez; Antonio Lopez |
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Title |
Cost-sensitive Structured SVM for Multi-category Domain Adaptation |
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Conference Article |
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Year |
2014 |
Publication |
22nd International Conference on Pattern Recognition |
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3886 - 3891 |
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Domain Adaptation; Pedestrian Detection |
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Domain adaptation addresses the problem of accuracy drop that a classifier may suffer when the training data (source domain) and the testing data (target domain) are drawn from different distributions. In this work, we focus on domain adaptation for structured SVM (SSVM). We propose a cost-sensitive domain adaptation method for SSVM, namely COSS-SSVM. In particular, during the re-training of an adapted classifier based on target and source data, the idea that we explore consists in introducing a non-zero cost even for correctly classified source domain samples. Eventually, we aim to learn a more targetoriented classifier by not rewarding (zero loss) properly classified source-domain training samples. We assess the effectiveness of COSS-SSVM on multi-category object recognition. |
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Stockholm; Sweden; August 2014 |
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IEEE |
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1051-4651 |
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ICPR |
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ADAS; 600.057; 600.054; 601.217; 600.076 |
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no |
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ADAS @ adas @ XRV2014a |
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2434 |
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Author |
Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
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Title |
Generic Subclass Ensemble: A Novel Approach to Ensemble Classification |
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Conference Article |
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Year |
2014 |
Publication |
22nd International Conference on Pattern Recognition |
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1254 - 1259 |
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Multiple classifier systems, also known as classifier ensembles, have received great attention in recent years because of their improved classification accuracy in different applications. In this paper, we propose a new general approach to ensemble classification, named generic subclass ensemble, in which each base classifier is trained with data belonging to a subset of classes, and thus discriminates among a subset of target categories. The ensemble classifiers are then fused using a combination rule. The proposed approach differs from existing methods that manipulate the target attribute, since in our approach individual classification problems are not restricted to two-class problems. We perform a series of experiments to evaluate the efficiency of the generic subclass approach on a set of benchmark datasets. Experimental results with multilayer perceptrons show that the proposed approach presents a viable alternative to the most commonly used ensemble classification approaches. |
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Stockholm; August 2014 |
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1051-4651 |
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HuPBA;MILAB |
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no |
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Admin @ si @ BGE2014b |
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2445 |
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Author |
Mohammad Ali Bagheri; Gang Hu; Qigang Gao; Sergio Escalera |
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Title |
A Framework of Multi-Classifier Fusion for Human Action Recognition |
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Conference Article |
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2014 |
Publication |
22nd International Conference on Pattern Recognition |
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1260 - 1265 |
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The performance of different action-recognition methods using skeleton joint locations have been recently studied by several computer vision researchers. However, the potential improvement in classification through classifier fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of five action learning techniques, each performing the recognition task from a different perspective. The underlying rationale of the fusion approach is that different learners employ varying structures of input descriptors/features to be trained. These varying structures cannot be attached and used by a single learner. In addition, combining the outputs of several learners can reduce the risk of an unfortunate selection of a poorly performing learner. This leads to having a more robust and general-applicable framework. Also, we propose two simple, yet effective, action description techniques. In order to improve the recognition performance, a powerful combination strategy is utilized based on the Dempster-Shafer theory, which can effectively make use of diversity of base learners trained on different sources of information. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers' output, showing advanced performance of the proposed methodology. |
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Stockholm; Sweden; August 2014 |
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1051-4651 |
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HuPBA;MILAB |
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Admin @ si @ BHG2014 |
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2446 |
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Author |
David Fernandez; Jon Almazan; Nuria Cirera; Alicia Fornes; Josep Llados |
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BH2M: the Barcelona Historical Handwritten Marriages database |
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Conference Article |
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2014 |
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22nd International Conference on Pattern Recognition |
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256 - 261 |
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This paper presents an image database of historical handwritten marriages records stored in the archives of Barcelona cathedral, and the corresponding meta-data addressed to evaluate the performance of document analysis algorithms. The contribution of this paper is twofold. First, it presents a complete ground truth which covers the whole pipeline of handwriting
recognition research, from layout analysis to recognition and understanding. Second, it is the first dataset in the emerging area of genealogical document analysis, where documents are manuscripts pseudo-structured with specific lexicons and the interest is beyond pure transcriptions but context dependent. |
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Creete Island; Grecia; September 2014 |
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1051-4651 |
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DAG; 600.056; 600.061; 602.006; 600.077 |
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Admin @ si @ FAC2014 |
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2461 |
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Lluis Gomez; Dimosthenis Karatzas |
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MSER-based Real-Time Text Detection and Tracking |
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Conference Article |
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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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1051-4651 |
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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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Author |
P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes |
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Title |
A Coarse-to-Fine Word Spotting Approach for Historical Handwritten Documents Based on Graph Embedding and Graph Edit Distance |
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Conference Article |
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2014 |
Publication |
22nd International Conference on Pattern Recognition |
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3074 - 3079 |
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word spotting; coarse-to-fine mechamism; graphbased representation; graph embedding; graph edit distance |
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Effective information retrieval on handwritten document images has always been a challenging task, especially historical ones. In the paper, we propose a coarse-to-fine handwritten word spotting approach based on graph representation. The presented model comprises both the topological and morphological signatures of the handwriting. Skeleton-based graphs with the Shape Context labelled vertexes are established for connected components. Each word image is represented as a sequence of graphs. Aiming at developing a practical and efficient word spotting approach for large-scale historical handwritten documents, a fast and coarse comparison is first applied to prune the regions that are not similar to the query based on the graph embedding methodology. Afterwards, the query and regions of interest are compared by graph edit distance based on the Dynamic Time Warping alignment. The proposed approach is evaluated on a public dataset containing 50 pages of historical marriage license records. The results show that the proposed approach achieves a compromise between efficiency and accuracy. |
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Stockholm; Sweden; August 2014 |
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1051-4651 |
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DAG; 600.061; 602.006; 600.077 |
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Admin @ si @ WEG2014a |
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2515 |
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Francisco Cruz; Oriol Ramos Terrades |
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Title |
EM-Based Layout Analysis Method for Structured Documents |
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Conference Article |
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Year |
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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ICPR |
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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 |
Thierry Brouard; Jordi Gonzalez; Caifeng Shan; Massimo Piccardi; Larry S. Davis |
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Title |
Special issue on background modeling for foreground detection in real-world dynamic scenes |
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Journal Article |
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Year |
2014 |
Publication |
Machine Vision and Applications |
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MVAP |
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25 |
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5 |
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1101-1103 |
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Although background modeling and foreground detection are not mandatory steps for computer vision applications, they may prove useful as they separate the primal objects usually called “foreground” from the remaining part of the scene called “background”, and permits different algorithmic treatment in the video processing field such as video surveillance, optical motion capture, multimedia applications, teleconferencing and human–computer interfaces. Conventional background modeling methods exploit the temporal variation of each pixel to model the background, and the foreground detection is made using change detection. The last decade witnessed very significant publications on background modeling but recently new applications in which background is not static, such as recordings taken from mobile devices or Internet videos, need new developments to detect robustly moving objects in challenging environments. Thus, effective methods for robustness to deal both with dynamic backgrounds, i |
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Springer Berlin Heidelberg |
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0932-8092 |
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ISE; 600.078 |
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BGS2014a |
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2411 |
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Author |
Fahad Shahbaz Khan; Shida Beigpour; Joost Van de Weijer; Michael Felsberg |
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Title |
Painting-91: A Large Scale Database for Computational Painting Categorization |
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Journal Article |
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Year |
2014 |
Publication |
Machine Vision and Applications |
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MVAP |
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25 |
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6 |
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1385-1397 |
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Computer analysis of visual art, especially paintings, is an interesting cross-disciplinary research domain. Most of the research in the analysis of paintings involve medium to small range datasets with own specific settings. Interestingly, significant progress has been made in the field of object and scene recognition lately. A key factor in this success is the introduction and availability of benchmark datasets for evaluation. Surprisingly, such a benchmark setup is still missing in the area of computational painting categorization. In this work, we propose a novel large scale dataset of digital paintings. The dataset consists of paintings from 91 different painters. We further show three applications of our dataset namely: artist categorization, style classification and saliency detection. We investigate how local and global features popular in image classification perform for the tasks of artist and style categorization. For both categorization tasks, our experimental results suggest that combining multiple features significantly improves the final performance. We show that state-of-the-art computer vision methods can correctly classify 50 % of unseen paintings to its painter in a large dataset and correctly attribute its artistic style in over 60 % of the cases. Additionally, we explore the task of saliency detection on paintings and show experimental findings using state-of-the-art saliency estimation algorithms. |
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Springer Berlin Heidelberg |
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0932-8092 |
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CIC; LAMP; 600.074; 600.079 |
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Admin @ si @ KBW2014 |
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2510 |
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