BHoG: binary descriptor for sketch-based image retrieval

Fu, Haiyan; Zhao, Hanguang; Kong, Xiangwei; Zhang, Xianbo

doi:10.1007/s00530-014-0406-9

Cited by 14 publications

(5 citation statements)

References 15 publications

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“…However, due to the fact that the HOG descriptor follows a pixel-wise strategy, the representation of a sketch image always produces many zeroes in the final histogram, since a sketch tends to be sparse by nature. Fu et al [16] also introduced an improvement on the HOG descriptor, namely, the Binary HOG descriptor (BHOG). This can compute the feature vectors more quickly than the HOG descriptor while also taking up less memory.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Web3D learning framework for 3D shape retrieval based on hybrid convolutional neural networks

Zhou

Jia

Huang

et al. 2020

Tinshhua Sci. Technol.

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With the rapid development of Web3D technologies, sketch-based model retrieval has become an increasingly important challenge, while the application of Virtual Reality and 3D technologies has made shape retrieval of furniture over a web browser feasible. In this paper, we propose a learning framework for shape retrieval based on two Siamese VGG-16 Convolutional Neural Networks (CNNs), and a CNN-based hybrid learning algorithm to select the best view for a shape. In this algorithm, the AlexNet and VGG-16 CNN architectures are used to perform classification tasks and to extract features, respectively. In addition, a feature fusion method is used to measure the similarity relation of the output features from the two Siamese networks. The proposed framework can provide new alternatives for furniture retrieval in the Web3D environment. The primary innovation is in the employment of deep learning methods to solve the challenge of obtaining the best view of 3D furniture, and to address cross-domain feature learning problems. We conduct an experiment to verify the feasibility of the framework and the results show our approach to be superior in comparison to many mainstream state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%

“…For training and testing of the dataset, we evaluate our method in comparison with PHOG [7] , SHELO [14] , HOG [15] , BHOG [16] , ORB, and SIFT. The experiment result based on the precision-recall curve can be seen in Fig.…”

Section: Experiments For Shape Retrievalmentioning

confidence: 99%

Web3D learning framework for 3D shape retrieval based on hybrid convolutional neural networks

Zhou

Jia

Huang

et al. 2020

Tinshhua Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Saavedra proposed the soft computer of HELO (S-HELO), it computer cell orientations in a soft manner using bilinear and tri-linear interpolation, and took into account spatial information, then, it computed an orientation histogram using weighted votes from the estimated cell orientations. Fu et al [18] also improved the HOG descriptor, namely, binary HOG descriptor (BHOG). It can be faster than Hog descriptor to compute the feature vectors and take up less memory.…”

Section: State-of-the-art Descriptormentioning

confidence: 99%

SVM: Sketch-based 3D Retrieval Application Using Classification Method

Zhou¹,

Jia²

2017

dtcse

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Abstract. With the rapid development of 3D technology, the demand to use and retrieve the 3D model is becoming more and more urgent. In this case, it is more and more import and necessary that sketch-based 3D model retrieval. The user provides a hand-drawn sketch, then the system can be provided the possible list of models, then the user can select the need model from this list. In this paper, it's proposed a framework of using SVM classification methods and K-means cluster algorithm. In preprocess part; the adaptive thinning algorithm is used to process the input sketch. In the offline part, the model is projected to multi-view images, and then SVM classifier has been used to classify these images. Moreover, we extract the images features, and use K-means algorithm to cluster these features. Finally, the feature dictionary will be acquired. In online process part, it only needs to extract the input sketch feature, and computes the distance between dictionary and their features. Besides, the experiment is realized to verify the feasibility of the approach. Finally, it was compared with other's approaches; the result shows that the approach is viable and robustness.

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“…For this reason, image retrieval which is used in many fields such as medicine and health, aeronautics and military has been an important research area [1][2][3]. Text based systems were initially launched for the image retrieval problem.…”

Section: Introductionmentioning

confidence: 99%

Content-Based Image Retrieval with Combined Features: Color and Gradient

Kılıçaslan

Demi̇rci̇

2021

Preprint

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Feature extraction is fundamental stage of effective content based image retrieval (CBIR). However, it remains challenging issue to extract low-level features for retrieval systems. This paper puts forward an effective solution proposal for the aforementioned problem. Initially, images and their gradients are clustered with multi-level thresholding. A codebook is generated with threshold values. The size of the codebook generated depends on the number of thresholds. Consequently, every pixel in color image is included in a cluster by means of the codebook. Color reduction is performed by assigning the average values of pixels in the same cluster. A cluster-based one-dimensional histogram (CBH) is created with the numbers of pixels in every cluster represented with a single color. Then the cluster-based feature vectors with histogram are extracted from original image and gradient image. Accordingly, relevant features are combined. The developed feature vector is called as combined feature vector (CFV). The most important advantages of CFV are that it performs an effective color reduction technique and feature presentation by processing texture information with gradient operator. Therefore, the main contribution of the combined feature vector suggested is its high accuracy and stability for image retrieval. The proposed method has been tested with Corel-1K, Corel-5K Corel-10K and GHIM-10K datasets. In addition, performances of different image histogram similarity techniques such as cosine, histogram intersection and Euclidean distance have been verified with the developed algorithm. Experimental results have been analyzed in two categories. Initially, CBIR results produced with combined feature vectors which are generated by Otsu, Kapur and center of gravity of histogram (CGH) procedures have been evaluated. Then, the CBIR strategy based on CGH method has been compared with CBIR systems with local binary pattern (LBP) and gradient-structures histogram (GSH). It was observed that CBIR approach based on CGH technique has significantly outperformed.

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BHoG: binary descriptor for sketch-based image retrieval

Cited by 14 publications

References 15 publications

Web3D learning framework for 3D shape retrieval based on hybrid convolutional neural networks

Web3D learning framework for 3D shape retrieval based on hybrid convolutional neural networks

SVM: Sketch-based 3D Retrieval Application Using Classification Method

Content-Based Image Retrieval with Combined Features: Color and Gradient

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