BoR: BAG-OF-RELATIONS FOR SYMBOL RETRIEVAL

Santosh, K. C.; Wendling, Laurent

doi:10.1142/s0218001414500177

Cited by 22 publications

(11 citation statements)

References 65 publications

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“…In architectural floor plan analysis, images are typically binary (bi-level) or grayscale and thus shape [49], as opposed to color for instance, is the most important visual cue for describing them. The main challenges present in this step are scale and rotation changes, [35,36,37,38] Scale and rotation invariant, robust to small variations Vectors and quadrilateral primitives V Hierarchical Plausibility Graph (HPG) [39,40] Robust to various distortions Critical points and lines V Shape, topology, and Region Adjacency Graph (RAG) [41,42] Rotation and scale invariant Image regions V Boundary and RAG [43] Rotation and scale invariant Image regions V Convexity and Near Convex Region Adjacency Graph (NCRAG) [44] Rotation and scale invariant Oriented line segments V Bag-of-GraphPaths (BoGP) [45] Rotation invariant Critical points V Jacobs' statistical grouping [46] Scale and rotation invariant Contour map V Bag-of-Relations (BoR) [47] Scale and rotation invariant and robust to irregularities Thick (solid) components, circles, corners and extremities V Cassinian ovals [48] Not invariant to scaling and rotation…”

Section: Symbol Spotting: Description Phasementioning

confidence: 99%

Geometry-based symbol spotting in born-digital architectural floor plans

2021

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Architectural floor plans are scale-accurate 2D drawings of one level of a building, seen from above, which convey structural and semantic information related to rooms, walls, symbols, textual data, etc. They consist of lines, curves, symbols, and textual markings, showing the relationships between rooms and all physical features, required for the proper construction or renovation of the building.First, this thesis provides a thorough study of state-of-the-art on symbol spotting methods for architectural drawings, an application domain providing the document image analysis and graphic recognition communities with an interesting set of challenges linked to the sheer complexity and density of embedded information, that have yet to be resolved.Second, we propose a hybrid method that capitalizes on strengths of both vector-based and pixel-based symbol spotting techniques. In the description phase, the salient geometric constituents of a symbol are extracted by a variety of vectorization techniques, including a proposed voting-based algorithm for finding partial ellipses. This enables us to better handle local shape irregularities and boundary discontinuities, as well as partial occlusion and overlap. In the matching phase, the spatial relationship between the geometric primitives is encoded via a primitive-aware proximity graph. A statistical approach is then used to rapidly yield a coarse localization of symbols within the plan. Localization is further refined with a pixel-based step implementing a modified cross-correlation function. Experimental results on the public SESYD synthetic dataset and real-world images demonstrate that our approach clearly outperforms other popular symbol spotting approaches.Traditional on-the-fly symbol spotting methods are unable to address the semantic challenge of graphical notation variability, i.e. low intra-class symbol similarity, an issue that is particularly important in architectural floor plan analysis. The presence of occlusion and clutter, characteristic of real-world plans, along with a varying graphical symbol complexity from almost trivial to highly complex, also pose challenges to existing spotting methods. iv Third, we address all the above issues by leveraging recent advances in deep learningbased neural networks and adapting an object detection framework based on the YOLO (You Only Look Once) architecture. We propose a training strategy based on tiles, avoiding many issues particular to deep learning-based object detection networks related to the relatively small size of symbols compared to entire floor plans, aspect ratios, and data augmentation. Experimental results demonstrate that our method successfully detects architectural symbols with low intra-class similarity and of variable graphical complexity,

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Section: Symbol Spotting: Description Phasementioning

confidence: 99%

Geometry-based symbol spotting in born-digital architectural floor plans

2021

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“…In the field of symbol recognition, the works of [9], [10] introduced bags-of-relations (BoR), an original way to produce vocabularies of spatial relations. The approach was applied on a well-controlled set of visual primitives specific to the application domain (e.g., circles, corners or extremities of symbols).…”

Section: Michaël Clément ; Mickaël Coustaty ; Camille Kurtz ; Laurentmentioning

confidence: 99%

Local Enlacement Histograms for Historical Drop Caps Style Recognition

Clément

Coustaty

Kurtz

et al. 2017

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)

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This article focuses on the specific issue of drop caps image recognition in the context of cultural heritage preservation. Due to their heterogeneity and their weakly structured properties, these historical images represent challenging data. An important aspect in the recognition process of drop caps is their background styles, which can be considered as discriminative features to identify both the printer and the period. Most existing methods for style recognition are based on low-level features such as color or texture properties. In this article, we present a novel framework for the recognition of drop caps style based on features of higher levels. We propose to capture the spatial structure carried by these images using relative position descriptors modeling the enlacement between local cells of pixel layers obtained from a document segmentation step. Such descriptors are then exploited in an efficient bag-of-features learning procedure. Experimental results obtained on a dataset of historical drop caps images highlight the interest of this approach, and in particular the benefit of considering spatial information.

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“…In the field of symbol recognition, recent works [21], [22] introduced bags-of-relations (BoR), an original way to produce composite vocabularies of spatial configurations. The approach was applied on a well-controlled set of visual primitives specific to the application domain (e.g., circles, corners or extremities).…”

Section: B Towards Bags-of-relationsmentioning

confidence: 99%

Bags of spatial relations and shapes features for structural object description

Clément

Kurtz

Wendling

2016

2016 23rd International Conference on Pattern Recognition (ICPR)

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We introduce a novel bags-of-features framework based on relative position descriptors, modeling both spatial relations and shape information between the pairwise structural subparts of objects. First, we propose a hierarchical approach for the decomposition of complex objects into structural subparts, as well as their description using the concept of Force Histogram Decomposition (FHD). Then, an original learning methodology is presented, in order to produce discriminative hierarchical spatial features for object classification tasks. The cornerstone is to build an homogeneous vocabulary of shapes and spatial configurations occurring across the objects at different scales of decomposition. An advantage of this learning procedure is its compatibility with traditional bags-of-features frameworks, allowing for hybrid representations of both structural and local features. Classification results obtained on two datasets of images highlight the interest of this approach based on hierarchical spatial relations descriptors to recognize structured objects.

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BoR: BAG-OF-RELATIONS FOR SYMBOL RETRIEVAL

Cited by 22 publications

References 65 publications

Geometry-based symbol spotting in born-digital architectural floor plans

Geometry-based symbol spotting in born-digital architectural floor plans

Local Enlacement Histograms for Historical Drop Caps Style Recognition

Bags of spatial relations and shapes features for structural object description

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