Architectural style classification of Mexican historical buildings using deep convolutional neural networks and sparse features

Obeso, Abraham Montoya; Benois-Pineau, Jenny; Acosta, Alejandro; Vázquez, Mireya Saraí García

doi:10.1117/1.jei.26.1.011016

Cited by 29 publications

(18 citation statements)

References 22 publications

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“…A recent paper proposes a model of classifying the architectural styles of historical Mexican buildings across three categories: Prehispanic, Colonial, and Modern [20]. Their method uses a deep convolutional neural network whose input is composed of sparse features in conjunction with primary color pixel values to increase its accuracy.…”

Section: Building Recognitionmentioning

confidence: 99%

“…To our knowledge, the majority of existing public image datasets related to the classification of buildings are based on satellite imagery. Nonetheless, two datasets with architectural-style classes and images of building facades were found online: one called MexCulture142, which contains 284 images of Mexican buildings divided into 142 subclasses of: prehispanic, colonial, and modern styles [20], and the other is a 25-class dataset of different architectural styles which contains 4794 images in total, with 60 to 300 per class [26]. However, only about 20% of all these images across the two datasets combined were appropriate for our research purpose.…”

Section: Proposed Datasetsmentioning

confidence: 99%

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HierarchyNet: Hierarchical CNN-Based Urban Building Classification

Taoufiq¹,

Nagy

Benedek

2020

Remote Sensing

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Automatic building categorization and analysis are particularly relevant for smart city applications and cultural heritage programs. Taking a picture of the facade of a building and instantly obtaining information about it can enable the automation of processes in urban planning, virtual city tours, and digital archiving of cultural artifacts. In this paper, we go beyond traditional convolutional neural networks (CNNs) for image classification and propose the HierarchyNet: a new hierarchical network for the classification of urban buildings from all across the globe into different main and subcategories from images of their facades. We introduce a coarse-to-fine hierarchy on the dataset and the model learns to simultaneously extract features and classify across both levels of hierarchy. We propose a new multiplicative layer, which is able to improve the accuracy of the finer prediction by considering the feedback signal of the coarse layers. We have quantitatively evaluated the proposed approach both on our proposed building datasets, as well as on various benchmark databases to demonstrate that the model is able to efficiently learn hierarchical information. The HierarchyNet model is able to outperform the state-of-the-art convolutional neural networks in urban building classification as well as in other multi-label classification tasks while using significantly fewer parameters.

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Section: Building Recognitionmentioning

confidence: 99%

Section: Proposed Datasetsmentioning

confidence: 99%

HierarchyNet: Hierarchical CNN-Based Urban Building Classification

Taoufiq¹,

Nagy

Benedek

2020

Remote Sensing

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“…In 2016, Obeso et al [ 39 ] presented a work based on convolutional neural network (CNN) using sparse features (SF) to classify images of buildings in conjunction with primary color pixel values (see Figure 9 ). As a result, their mode achieved of 88.01% accuracy.…”

Section: Related Workmentioning

confidence: 99%

“… CNN’s architecture (our own redrawing), conformed by four convolutional layers, three pooling layers, two normalization layers and two fully-connected layers at the end [ 39 ]. …”

Section: Figurementioning

confidence: 99%

A Visual Sensing Concept for Robustly Classifying House Types through a Convolutional Neural Network Architecture Involving a Multi-Channel Features Extraction

Tavakkoli

Mohsenzadegan

Kyamakya

2020

Sensors

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The core objective of this paper is to develop and validate a comprehensive visual sensing concept for robustly classifying house types. Previous studies regarding this type of classification show that this type of classification is not simple (i.e., tough) and most classifier models from the related literature have shown a relatively low performance. For finding a suitable model, several similar classification models based on convolutional neural network have been explored. We have found out that adding/involving/extracting better and more complex features result in a significant accuracy related performance improvement. Therefore, a new model taking this finding into consideration has been developed, tested and validated. The model developed is benchmarked with selected state-of-art classification models of relevance for the “house classification” endeavor. The test results obtained in this comprehensive benchmarking clearly demonstrate and validate the effectiveness and the superiority of our here developed deep-learning model. Overall, one notices that our model reaches classification performance figures (accuracy, precision, etc.) which are at least 8% higher (which is extremely significant in the ranges above 90%) than those reached by the previous state-of-the-art methods involved in the conducted comprehensive benchmarking.

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“…In more detail, studies have taken interest in fine-grained recognition of trees [1,2], flowers [3,4], and fruits [5,6] in plants, taking various devices to capture optical and multispectral images on the ground as well as using aerial filming as the resources and designed various variant CNNs to recognize targets with a single leaf and a cluster plant. In architecture-style analyzation, studies collect the images with the optical digital single-lens reflex camera to capture the entire appearance and design the DCNN for fine-grained classification [7,8]. Some scholars collect the optical image with a surveillance camera to recognize the rainfall intensity [9,10], and parts use the satellite image to classify and predict [11,12].…”

Section: Introductionmentioning

confidence: 99%

Object–Part Registration–Fusion Net for Fine-Grained Image Classification

Lin

Liu

2021

Symmetry

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Classifying fine-grained categories (e.g., bird species, car, and aircraft types) is a crucial problem in image understanding and is difficult due to intra-class and inter-class variance. Most of the existing fine-grained approaches individually utilize various parts and local information of objects to improve the classification accuracy but neglect the mechanism of the feature fusion between the object (global) and object’s parts (local) to reinforce fine-grained features. In this paper, we present a novel framework, namely object–part registration–fusion Net (OR-Net), which considers the mechanism of registration and fusion between an object (global) and its parts’ (local) features for fine-grained classification. Our model learns the fine-grained features from the object of global and local regions and fuses these features with the registration mechanism to reinforce each region’s characteristics in the feature maps. Precisely, OR-Net consists of: (1) a multi-stream feature extraction net, which generates features with global and various local regions of objects; (2) a registration–fusion feature module calculates the dimension and location relationships between global (object) regions and local (parts) regions to generate the registration information and fuses the local features into the global features with registration information to generate the fine-grained feature. Experiments execute symmetric GPU devices with symmetric mini-batch to verify that OR-Net surpasses the state-of-the-art approaches on CUB-200-2011 (Birds), Stanford-Cars, and Stanford-Aircraft datasets.

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Architectural style classification of Mexican historical buildings using deep convolutional neural networks and sparse features

Cited by 29 publications

References 22 publications

HierarchyNet: Hierarchical CNN-Based Urban Building Classification

HierarchyNet: Hierarchical CNN-Based Urban Building Classification

A Visual Sensing Concept for Robustly Classifying House Types through a Convolutional Neural Network Architecture Involving a Multi-Channel Features Extraction

Object–Part Registration–Fusion Net for Fine-Grained Image Classification

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