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Author |
Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas |
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Title |
Learning to Learn from Web Data through Deep Semantic Embeddings |
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2018 |
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15th European Conference on Computer Vision Workshops |
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11134 |
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514-529 |
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In this paper we propose to learn a multimodal image and text embedding from Web and Social Media data, aiming to leverage the semantic knowledge learnt in the text domain and transfer it to a visual model for semantic image retrieval. We demonstrate that the pipeline can learn from images with associated text without supervision and perform a thourough analysis of five different text embeddings in three different benchmarks. We show that the embeddings learnt with Web and Social Media data have competitive performances over supervised methods in the text based image retrieval task, and we clearly outperform state of the art in the MIRFlickr dataset when training in the target data. Further we demonstrate how semantic multimodal image retrieval can be performed using the learnt embeddings, going beyond classical instance-level retrieval problems. Finally, we present a new dataset, InstaCities1M, composed by Instagram images and their associated texts that can be used for fair comparison of image-text embeddings. |
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Munich; Alemanya; September 2018 |
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ECCVW |
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DAG; 600.129; 601.338; 600.121 |
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Admin @ si @ GGG2018a |
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3175 |
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Author |
Arka Ujjal Dey; Suman Ghosh; Ernest Valveny |
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Title |
Don't only Feel Read: Using Scene text to understand advertisements |
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2018 |
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IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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We propose a framework for automated classification of Advertisement Images, using not just Visual features but also Textual cues extracted from embedded text. Our approach takes inspiration from the assumption that Ad images contain meaningful textual content, that can provide discriminative semantic interpretetion, and can thus aid in classifcation tasks. To this end, we develop a framework using off-the-shelf components, and demonstrate the effectiveness of Textual cues in semantic Classfication tasks. |
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Salt Lake City; Utah; USA; June 2018 |
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CVPRW |
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DAG; 600.121; 600.129 |
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Admin @ si @ DGV2018 |
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3551 |
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Author |
Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Marçal Rusiñol; Francesc J. Ferri |
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Title |
Fast Kernel Generalized Discriminative Common Vectors for Feature Extraction |
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2018 |
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Journal of Mathematical Imaging and Vision |
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JMIV |
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60 |
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4 |
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512-524 |
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This paper presents a supervised subspace learning method called Kernel Generalized Discriminative Common Vectors (KGDCV), as a novel extension of the known Discriminative Common Vectors method with Kernels. Our method combines the advantages of kernel methods to model complex data and solve nonlinear
problems with moderate computational complexity, with the better generalization properties of generalized approaches for large dimensional data. These attractive combination makes KGDCV specially suited for feature extraction and classification in computer vision, image processing and pattern recognition applications. Two different approaches to this generalization are proposed, a first one based on the kernel trick (KT) and a second one based on the nonlinear projection trick (NPT) for even higher efficiency. Both methodologies
have been validated on four different image datasets containing faces, objects and handwritten digits, and compared against well known non-linear state-of-art methods. Results show better discriminant properties than other generalized approaches both linear or kernel. In addition, the KGDCV-NPT approach presents a considerable computational gain, without compromising the accuracy of the model. |
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DAG; ADAS; 600.086; 600.130; 600.121; 600.118; 600.129 |
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Admin @ si @ DMH2018a |
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3062 |
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Author |
Sounak Dey; Anjan Dutta; Juan Ignacio Toledo; Suman Ghosh; Josep Llados; Umapada Pal |
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Title |
SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification |
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Miscellaneous |
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2018 |
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Arxiv |
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Offline signature verification is one of the most challenging tasks in biometrics and document forensics. Unlike other verification problems, it needs to model minute but critical details between genuine and forged signatures, because a skilled falsification might often resembles the real signature with small deformation. This verification task is even harder in writer independent scenarios which is undeniably fiscal for realistic cases. In this paper, we model an offline writer independent signature verification task with a convolutional Siamese network. Siamese networks are twin networks with shared weights, which can be trained to learn a feature space where similar observations are placed in proximity. This is achieved by exposing the network to a pair of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing it between dissimilar pairs. Experiments conducted on cross-domain datasets emphasize the capability of our network to model forgery in different languages (scripts) and handwriting styles. Moreover, our designed Siamese network, named SigNet, exceeds the state-of-the-art results on most of the benchmark signature datasets, which paves the way for further research in this direction. |
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DAG; 600.097; 600.121 |
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Admin @ si @ DDT2018 |
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3085 |
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Author |
Dena Bazazian; Dimosthenis Karatzas; Andrew Bagdanov |
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Title |
Soft-PHOC Descriptor for End-to-End Word Spotting in Egocentric Scene Images |
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Conference Article |
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2018 |
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International Workshop on Egocentric Perception, Interaction and Computing at ECCV |
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Word spotting in natural scene images has many applications in scene understanding and visual assistance. We propose Soft-PHOC, an intermediate representation of images based on character probability maps. Our representation extends the concept of the Pyramidal Histogram Of Characters (PHOC) by exploiting Fully Convolutional Networks to derive a pixel-wise mapping of the character distribution within candidate word regions. We show how to use our descriptors for word spotting tasks in egocentric camera streams through an efficient text line proposal algorithm. This is based on the Hough Transform over character attribute maps followed by scoring using Dynamic Time Warping (DTW). We evaluate our results on ICDAR 2015 Challenge 4 dataset of incidental scene text captured by an egocentric camera. |
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Munich; Alemanya; September 2018 |
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ECCVW |
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DAG; 600.129; 600.121; |
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Admin @ si @ BKB2018b |
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3174 |
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Author |
Sangheeta Roy; Palaiahnakote Shivakumara; Namita Jain; Vijeta Khare; Anjan Dutta; Umapada Pal; Tong Lu |
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Title |
Rough-Fuzzy based Scene Categorization for Text Detection and Recognition in Video |
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Journal Article |
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2018 |
Publication |
Pattern Recognition |
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PR |
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80 |
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64-82 |
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Rough set; Fuzzy set; Video categorization; Scene image classification; Video text detection; Video text recognition |
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Scene image or video understanding is a challenging task especially when number of video types increases drastically with high variations in background and foreground. This paper proposes a new method for categorizing scene videos into different classes, namely, Animation, Outlet, Sports, e-Learning, Medical, Weather, Defense, Economics, Animal Planet and Technology, for the performance improvement of text detection and recognition, which is an effective approach for scene image or video understanding. For this purpose, at first, we present a new combination of rough and fuzzy concept to study irregular shapes of edge components in input scene videos, which helps to classify edge components into several groups. Next, the proposed method explores gradient direction information of each pixel in each edge component group to extract stroke based features by dividing each group into several intra and inter planes. We further extract correlation and covariance features to encode semantic features located inside planes or between planes. Features of intra and inter planes of groups are then concatenated to get a feature matrix. Finally, the feature matrix is verified with temporal frames and fed to a neural network for categorization. Experimental results show that the proposed method outperforms the existing state-of-the-art methods, at the same time, the performances of text detection and recognition methods are also improved significantly due to categorization. |
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DAG; 600.097; 600.121 |
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Admin @ si @ RSJ2018 |
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3096 |
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Author |
Lluis Gomez; Marçal Rusiñol; Ali Furkan Biten; Dimosthenis Karatzas |
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Title |
Subtitulació automàtica d'imatges. Estat de l'art i limitacions en el context arxivístic |
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2018 |
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Jornades Imatge i Recerca |
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JIR |
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DAG; 600.084; 600.135; 601.338; 600.121; 600.129 |
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Admin @ si @ GRB2018 |
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3173 |
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Author |
Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas |
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Title |
Cutting Sayre's Knot: Reading Scene Text without Segmentation. Application to Utility Meters |
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2018 |
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13th IAPR International Workshop on Document Analysis Systems |
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97-102 |
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Robust Reading; End-to-end Systems; CNN; Utility Meters |
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In this paper we present a segmentation-free system for reading text in natural scenes. A CNN architecture is trained in an end-to-end manner, and is able to directly output readings without any explicit text localization step. In order to validate our proposal, we focus on the specific case of reading utility meters. We present our results in a large dataset of images acquired by different users and devices, so text appears in any location, with different sizes, fonts and lengths, and the images present several distortions such as
dirt, illumination highlights or blur. |
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Viena; Austria; April 2018 |
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DAS |
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DAG; 600.084; 600.121; 600.129 |
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Admin @ si @ GRK2018 |
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3102 |
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Dimosthenis Karatzas; Lluis Gomez; Marçal Rusiñol; Anguelos Nicolaou |
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Title |
The Robust Reading Competition Annotation and Evaluation Platform |
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2018 |
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13th IAPR International Workshop on Document Analysis Systems |
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61-66 |
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The ICDAR Robust Reading Competition (RRC), initiated in 2003 and reestablished in 2011, has become the defacto evaluation standard for the international community. Concurrent with its second incarnation in 2011, a continuous
effort started to develop an online framework to facilitate the hosting and management of competitions. This short paper briefly outlines the Robust Reading Competition Annotation and Evaluation Platform, the backbone of the
Robust Reading Competition, comprising a collection of tools and processes that aim to simplify the management and annotation of data, and to provide online and offline performance evaluation and analysis services. |
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Viena; Austria; April 2018 |
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DAG; 600.084; 600.121 |
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KGR2018 |
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3103 |
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David Aldavert; Marçal Rusiñol |
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Manuscript text line detection and segmentation using second-order derivatives analysis |
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2018 |
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13th IAPR International Workshop on Document Analysis Systems |
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293 - 298 |
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text line detection; text line segmentation; text region detection; second-order derivatives |
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In this paper, we explore the use of second-order derivatives to detect text lines on handwritten document images. Taking advantage that the second derivative gives a minimum response when a dark linear element over a
bright background has the same orientation as the filter, we use this operator to create a map with the local orientation and strength of putative text lines in the document. Then, we detect line segments by selecting and merging the filter responses that have a similar orientation and scale. Finally, text lines are found by merging the segments that are within the same text region. The proposed segmentation algorithm, is learning-free while showing a performance similar to the state of the art methods in publicly available datasets. |
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Viena; Austria; April 2018 |
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DAG; 600.084; 600.129; 302.065; 600.121 |
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Admin @ si @ AlR2018a |
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3104 |
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