Consistent line clusters for building recognition in CBIR

Li, Yi; Shapiro, Linda G.

doi:10.1109/icpr.2002.1048195

Cited by 41 publications

(4 citation statements)

References 9 publications

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“…In their research, Li & Shapiro (2002) used color orientation and spatial information for each line segment of the image. These segments were integrated and grouped into a mid-level feature type, originating the consistent line clusters.…”

Section: Methodsmentioning

confidence: 99%

“…How to deal with this challenge is a current research problem, whose solution has been addressed to few building recognition systems proposed in recent years (Li & Allinson, 2013), (Li & Allinson, 2009), (Hutchings & Cuevas, 2005), (Zhang & Kosecka, 2005), (Groeneweg at al., 2006), (Li & Shapiro, 2002), (Trinh at al., 2002), (Duan & Allinson, 2010), (Chung at al., 2010), (Silva at al., 2022), all of which use high resolution images and some even with very fast algorithms. However, such techniques cannot deal with many images because they work with high resolution techniques.…”

Section: Introductionmentioning

confidence: 99%

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Recognition of low-resolution building images using deep learning and softmax

Da Silva,

Vasconcelos,

Teixeira

et al. 2023

OLEL

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Image recognition of buildings has been gaining attention in recent years due to the growing popularity of georeferencing tools and their use in the areas of tourism and navigation. The use of mobile devices is critical in these applications and, therefore, processing should be fast and allow the use of low-resolution images. However, many options to perform this task use highly complex recognition techniques. Thus, the great difficulty is that these images are obtained from different angles, under different lighting conditions, in addition to partial obstructions from trees, moving vehicles, or other buildings that hinder recognition. To bypass these problems, this work aims to use the Deep Stacked Autoencoder in the extraction and reduction of features, the Softmax classifier in the recognition and the same deep learning technique applied to the classifier that uses the multilayer perceptron network for comparison purposes with Softmax software. To verify the efficiency of the method, a database of images of buildings from the University of Shefield was used. The algorithm proved to be efficient in the recognition task, reaching 94.9% accuracy.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recognition of low-resolution building images using deep learning and softmax

Da Silva,

Vasconcelos,

Teixeira

et al. 2023

OLEL

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“…Li and Shapiro [4] used the consistent line cluster for content-based image retrieval. Specifically, the color, orientation, and spatial features of line segments are exploited to group image into line clusters.…”

Section: Introductionmentioning

confidence: 99%

Histogram of Oriented Gradient Based Gist Feature for Building Recognition

Cheng

2016

Computational Intelligence and Neuroscience

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We proposed a new method of gist feature extraction for building recognition and named the feature extracted by this method as the histogram of oriented gradient based gist (HOG-gist). The proposed method individually computes the normalized histograms of multiorientation gradients for the same image with four different scales. The traditional approach uses the Gabor filters with four angles and four different scales to extract orientation gist feature vectors from an image. Our method, in contrast, uses the normalized histogram of oriented gradient as orientation gist feature vectors of the same image. These HOG-based orientation gist vectors, combined with intensity and color gist feature vectors, are the proposed HOG-gist vectors. In general, the HOG-gist contains four multiorientation histograms (four orientation gist feature vectors), and its texture description ability is stronger than that of the traditional gist using Gabor filters with four angles. Experimental results using Sheffield Buildings Database verify the feasibility and effectiveness of the proposed HOG-gist.

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“…Recent developments have used middle-and high-level information to improve the low-level features. Li et al [84] have performed architectonics building recognition using color, orientation, and spatial features of line segments. Raghavan et al [163] have designed a similarity-preserving space transformation method of low-level image space into a high-level vector space to improve retrieval.…”

Section: Image Categorizationmentioning

confidence: 99%

Classificadores e aprendizado em processamento de imagens e visão computacional

Rocha¹

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Neste trabalho de doutorado, propomos a utilização de classificadores e técnicas de aprendizado de máquina para extrair informações relevantes de um conjunto de dados (e.g., imagens) para solução de alguns problemas em Processamento de Imagens e Visão Computacional.Os problemas de nosso interesse são: categorização de imagens em duas ou mais classes, detecção de mensagens escondidas, distinção entre imagens digitalmente adulteradas e imagens naturais, autenticação, multi-classificação, entre outros.Inicialmente, apresentamos uma revisão comparativa e crítica do estado da arte em análise forense de imagens e detecção de mensagens escondidas em imagens. Nosso objetivo é mostrar as potencialidades das técnicas existentes e, mais importante, apontar suas limitações. Com esse estudo, mostramos que boa parte dos problemas nessa área apontam para dois pontos em comum: a seleção de características e as técnicas de aprendizado a serem utilizadas. Nesse estudo, também discutimos questões legais associadas à análise forense de imagens como, por exemplo, o uso de fotografias digitais por criminosos.Em seguida, introduzimos uma técnica para análise forense de imagens testada no contexto de detecção de mensagens escondidas e de classificação geral de imagens em categorias como indoors, outdoors, geradas em computador e obras de arte.Ao estudarmos esse problema de multi-classificação, surgem algumas questões: como resolver um problema multi-classe de modo a poder combinar, por exemplo, características de classificação de imagens baseadas em cor, textura, forma e silhueta, sem nos preocuparmos demasiadamente em como normalizar o vetor-comum de características gerado? Como utilizar diversos classificadores diferentes, cada um, especializado e melhor configurado para um conjunto de características ou classes em confusão? Nesse sentido, apresentamos, uma técnica para fusão de classificadores e características no cenário multi-classe através da combinação de classificadores binários. Nós validamos nossa abordagem numa aplicação real para classificação automática de frutas e legumes.Finalmente, nos deparamos com mais um problema interessante: como tornar a utilização de poderosos classificadores binários no contexto multi-classe mais eficiente e eficaz? Assim, introduzimos uma técnica para combinação de classificadores binários (chamados classificadores base) para a resolução de problemas no contexto geral de multi-classificação. vii

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Consistent line clusters for building recognition in CBIR

Abstract: Abstract

Cited by 41 publications

References 9 publications

Recognition of low-resolution building images using deep learning and softmax

Recognition of low-resolution building images using deep learning and softmax

Histogram of Oriented Gradient Based Gist Feature for Building Recognition

Classificadores e aprendizado em processamento de imagens e visão computacional

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