An Improved Histogram of Edge Local Orientations for Sketch-Based Image Retrieval

Saavedra, José M.; Bustos, Benjamín

doi:10.1007/978-3-642-15986-2_44

Cited by 50 publications

(50 citation statements)

References 13 publications

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“…Fig. 3 is the comparison of first tier precision between our approach and the state of the art in sketch based 3D model retrieval like HELO [7] and STELA [8]. From 13 model classes, the first tier precision of our proposed approach is already better than the previous approaches in nine classes.…”

Section: Fig 2 3d Model Retrieval From Various Sketched Query Imagesmentioning

confidence: 88%

Sketch-based 3D model retrieval using compressive sensing classification

Yoon

Kuijper

2011

Electron. Lett.

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A sketch-based 3D model retrieval methodology by using compressive sensing (CS) is presented. The approach to search and automatically return a set of 3D mesh models from a large database consists of histogram of oriented gradient feature extraction from projected images of 3D models, and CS based classification technique. Experimental results show that the proposed 3D model retrieval is very efficient to search for the 3D models from user-drawn sketches including the variation of shape, pose, and view-points

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Section: Fig 2 3d Model Retrieval From Various Sketched Query Imagesmentioning

confidence: 88%

Sketch-based 3D model retrieval using compressive sensing classification

Yoon

Kuijper

2011

Electron. Lett.

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“…Due to the point by point matching scheme, SC is robust against noise yet considerably time-consuming [25]. Moreover, the SC method needs a sketch points sampling policy, which has been shown in other works [26] to be a critical parameter to set, and that it is highly dependent to the size of the sketch [27]. In addition, the size of the feature extraction window is static and not adaptive to the scale of the image, which makes SC not scaleinvariant.…”

Section: B State-of-the-art Reviewmentioning

confidence: 98%

Sketch-Based Image Retrieval by Size-Adaptive and Noise-Robust Feature Description

Chatbri

Kameyama

Kwan

2013

2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

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We review available methods for Sketch-Based Image Retrieval (SBIR) and we discuss their limitations. Then, we present two SBIR algorithms: The first algorithm extracts shape features by using support regions calculated for each sketch point, and the second algorithm adapts the Shape Context descriptor [1] to make it scale invariant and enhances its performance in presence of noise. Both algorithms share the property of calculating the feature extraction window according to the sketch size. Experiments and comparative evaluation with state-of-theart methods show that the proposed algorithms are competitive in distinctiveness capability and robust against noise.

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“…So far, sketch retrieval had formed 8 benchmark, they were as following: Snograss and Vanderwart [12] proposed the standard line drawings(1980), Cole et al [13]'s line drawing benchmark (2008), Saavedra and Bustos [14]'s sketch dataset (2010), Yoon et al [15]'s sketch-based 3D model retrieval benchmark (2010), Eitz et al [9]'s sketch-based shape retrieval benchmark, Eitz et al's [10] sketch recognition benchmark(2012), Small-scale benchmark [11]: Shrec'12 Sketch Track Benchmark, large-scale: Shrec'13 Sketch Track Benchmark [16].These benchmarks played an important role in the research and application of sketch retrieval.…”

Section: The Related Researchmentioning

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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An Improved Histogram of Edge Local Orientations for Sketch-Based Image Retrieval

Cited by 50 publications

References 13 publications

Sketch-based 3D model retrieval using compressive sensing classification

Sketch-based 3D model retrieval using compressive sensing classification

Sketch-Based Image Retrieval by Size-Adaptive and Noise-Robust Feature Description

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

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