A unified framework for semantic matching of architectural floorplans

Sharma, Divya; Chattopadhyay, Chiranjoy; Harit, Gaurav

doi:10.1109/icpr.2016.7899999

Cited by 23 publications

(20 citation statements)

References 18 publications

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“…The maximum accuracy obtained fora particular symbol is depicted in bold face numbers. It is clear thatperformance between ours and [50] iscomparable, except for large sink and tub, where SUGAMAN is superior. Thetechnique proposed in [50] could notdetect round table, while SUGAMAN achieves 82.35% accuracy.…”

Section: Comparative Analysismentioning

confidence: 81%

“…We have adopted the technique proposed in [50] for the identification of rooms. Walls are detected by performing morphological closing on the input floor plan image

I

(see Fig.…”

Section: Semantic Segmentation and Room Classificationmentioning

confidence: 99%

“…Table 6 shows thecomparison between existing techniques [25, 50, 54] and ours. All the decor item in each room image isclassified in one of the 12 decor categories.…”

Section: Comparative Analysismentioning

confidence: 99%

“…5 shows the 12 decor symbols used in the data set [47]. We have improved the technique of decor characterisation proposed in [50] by applying sequence of morphological operations. The technique in [50] uses a normalised area ratio of largest three components of a decor symbol for classification and characterisation of decors.…”

Section: Semantic Segmentation and Room Classificationmentioning

confidence: 99%

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SUGAMAN: describing floor plans for visually impaired by annotation learning and proximity‐based grammar

et al. 2019

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In this study, the authors propose a framework SUGAMAN (Supervised and Unified framework using Grammar and Annotation Model for Access and Navigation). SUGAMAN is a Hindi word meaning ‘easy passage from one place to another’. SUGAMAN synthesises textual description from a given floor plan image, usable by visually impaired to navigate by understanding the arrangement of rooms and furniture. It is the first framework for describing a floor plan and giving direction for obstacle‐free movement within a building. The model learns five classes of room categories from 1355 room image samples under a supervised learning paradigm. These learned annotations are fed into a description synthesis framework to yield a holistic description of a floor plan image. Authors demonstrate the performance of various supervised classifiers on room learning and provided a comparative analysis of system generated and human‐written descriptions. The contribution of this study includes a novel framework for description generation from document images with graphics while proposing a new feature representing the floor plans, text annotations for a publicly available data set, and an algorithm for door to door obstacle avoidance navigation. This work can be applied to areas like understanding floor plans and design of historical monuments, and retrieval.

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Section: Comparative Analysismentioning

confidence: 81%

“…We have adopted the technique proposed in [50] for the identification of rooms. Walls are detected by performing morphological closing on the input floor plan image

I

(see Fig.…”

Section: Semantic Segmentation and Room Classificationmentioning

confidence: 99%

“…Table 6 shows thecomparison between existing techniques [25, 50, 54] and ours. All the decor item in each room image isclassified in one of the 12 decor categories.…”

Section: Comparative Analysismentioning

confidence: 99%

Section: Semantic Segmentation and Room Classificationmentioning

confidence: 99%

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SUGAMAN: describing floor plans for visually impaired by annotation learning and proximity‐based grammar

et al. 2019

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“…The doors inside the layout are present in various orientations. We detect them [12] and close the gaps at their locations so as to construct definite boundaries for the rooms. This detects the walls in the layout to represent the room structures and thus delineates the rooms within the layout from each other.…”

Section: High‐level Semantic Feature Extractionmentioning

confidence: 99%

High‐level feature aggregation for fine‐grained architectural floor plan retrieval

Sharma

Chattopadhyay

2018

IET Computer Vision

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Due to the massive growth of real estate industry, there is an increase in the number of online platforms designed for finding homes/furnished properties. Instead of descriptive words, query by example is always a preferred method for retrieval. Floor plans are the basic 2D representation giving an idea about the building structure at a particular level. The authors propose a framework for the retrieval of similar architectural floor plans under the query by example paradigm. They propose a novel algorithm to extract high-level semantic features from an architectural floor plan. Fine-grained retrieval using weighted sum of the features is proposed, where a feature can be given more preference over others, during retrieval. Experiments were performed on publicly available dataset containing 510 floor plans and compared with existing state-of-the-art techniques. Their proposed method outperforms others both in qualitative and quantitative terms.

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Semantic Segmentation and Topological Mapping of Floor Plans

Liu

Huang

2021

Intelligent Robotics and Applications

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A unified framework for semantic matching of architectural floorplans

Cited by 23 publications

References 18 publications

SUGAMAN: describing floor plans for visually impaired by annotation learning and proximity‐based grammar

SUGAMAN: describing floor plans for visually impaired by annotation learning and proximity‐based grammar

High‐level feature aggregation for fine‐grained architectural floor plan retrieval

Semantic Segmentation and Topological Mapping of Floor Plans

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