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Pau Riba; Josep Llados; Alicia Fornes |
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Hierarchical graphs for coarse-to-fine error tolerant matching |
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2020 |
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Pattern Recognition Letters |
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PRL |
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134 |
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116-124 |
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Hierarchical graph representation; Coarse-to-fine graph matching; Graph-based retrieval |
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During the last years, graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their ability to capture both structural and appearance-based information. Thus, they provide a greater representational power than classical statistical frameworks. However, graph-based representations leads to high computational complexities usually dealt by graph embeddings or approximated matching techniques. Despite their representational power, they are very sensitive to noise and small variations of the input image. With the aim to cope with the time complexity and the variability present in the generated graphs, in this paper we propose to construct a novel hierarchical graph representation. Graph clustering techniques adapted from social media analysis have been used in order to contract a graph at different abstraction levels while keeping information about the topology. Abstract nodes attributes summarise information about the contracted graph partition. For the proposed representations, a coarse-to-fine matching technique is defined. Hence, small graphs are used as a filtering before more accurate matching methods are applied. This approach has been validated in real scenarios such as classification of colour images or retrieval of handwritten words (i.e. word spotting). |
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DAG; 600.097; 601.302; 603.057; 600.140; 600.121 |
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Admin @ si @ RLF2020 |
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3349 |
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Author |
Arka Ujjal Dey; Suman Ghosh; Ernest Valveny; Gaurav Harit |
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Title |
Beyond Visual Semantics: Exploring the Role of Scene Text in Image Understanding |
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Journal Article |
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Year |
2021 |
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Pattern Recognition Letters |
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PRL |
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149 |
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164-171 |
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Images with visual and scene text content are ubiquitous in everyday life. However, current image interpretation systems are mostly limited to using only the visual features, neglecting to leverage the scene text content. In this paper, we propose to jointly use scene text and visual channels for robust semantic interpretation of images. We do not only extract and encode visual and scene text cues, but also model their interplay to generate a contextual joint embedding with richer semantics. The contextual embedding thus generated is applied to retrieval and classification tasks on multimedia images, with scene text content, to demonstrate its effectiveness. In the retrieval framework, we augment our learned text-visual semantic representation with scene text cues, to mitigate vocabulary misses that may have occurred during the semantic embedding. To deal with irrelevant or erroneous recognition of scene text, we also apply query-based attention to our text channel. We show how the multi-channel approach, involving visual semantics and scene text, improves upon state of the art. |
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DAG; 600.121 |
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Admin @ si @ DGV2021 |
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3364 |
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Manuel Carbonell; Alicia Fornes; Mauricio Villegas; Josep Llados |
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Title |
A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages |
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Journal Article |
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2020 |
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Pattern Recognition Letters |
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PRL |
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136 |
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219-227 |
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In the last years, the consolidation of deep neural network architectures for information extraction in document images has brought big improvements in the performance of each of the tasks involved in this process, consisting of text localization, transcription, and named entity recognition. However, this process is traditionally performed with separate methods for each task. In this work we propose an end-to-end model that combines a one stage object detection network with branches for the recognition of text and named entities respectively in a way that shared features can be learned simultaneously from the training error of each of the tasks. By doing so the model jointly performs handwritten text detection, transcription, and named entity recognition at page level with a single feed forward step. We exhaustively evaluate our approach on different datasets, discussing its advantages and limitations compared to sequential approaches. The results show that the model is capable of benefiting from shared features by simultaneously solving interdependent tasks. |
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DAG; 600.140; 601.311; 600.121 |
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Admin @ si @ CFV2020 |
Serial |
3451 |
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Author |
B. Gautam; Oriol Ramos Terrades; Joana Maria Pujadas-Mora; Miquel Valls-Figols |
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Title |
Knowledge graph based methods for record linkage |
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Journal Article |
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Year |
2020 |
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Pattern Recognition Letters |
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PRL |
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136 |
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127-133 |
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Nowadays, it is common in Historical Demography the use of individual-level data as a consequence of a predominant life-course approach for the understanding of the demographic behaviour, family transition, mobility, etc. Advanced record linkage is key since it allows increasing the data complexity and its volume to be analyzed. However, current methods are constrained to link data from the same kind of sources. Knowledge graph are flexible semantic representations, which allow to encode data variability and semantic relations in a structured manner.
In this paper we propose the use of knowledge graph methods to tackle record linkage tasks. The proposed method, named WERL, takes advantage of the main knowledge graph properties and learns embedding vectors to encode census information. These embeddings are properly weighted to maximize the record linkage performance. We have evaluated this method on benchmark data sets and we have compared it to related methods with stimulating and satisfactory results. |
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DAG; 600.140; 600.121 |
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no |
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Admin @ si @ GRP2020 |
Serial |
3453 |
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Author |
Sounak Dey; Anguelos Nicolaou; Josep Llados; Umapada Pal |
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Title |
Evaluation of the Effect of Improper Segmentation on Word Spotting |
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Journal Article |
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Year |
2019 |
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International Journal on Document Analysis and Recognition |
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IJDAR |
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22 |
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361-374 |
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Word spotting is an important recognition task in large-scale retrieval of document collections. In most of the cases, methods are developed and evaluated assuming perfect word segmentation. In this paper, we propose an experimental framework to quantify the goodness that word segmentation has on the performance achieved by word spotting methods in identical unbiased conditions. The framework consists of generating systematic distortions on segmentation and retrieving the original queries from the distorted dataset. We have tested our framework on several established and state-of-the-art methods using George Washington and Barcelona Marriage Datasets. The experiments done allow for an estimate of the end-to-end performance of word spotting methods. |
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DAG; 600.097; 600.084; 600.121; 600.140; 600.129 |
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Admin @ si @ DNL2019 |
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3455 |
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