A two-stage shape retrieval (TSR) method with global and local features

Pan, Xiaqing; Chachada, Sachin; Kuo, C.-C. Jay

doi:10.1016/j.jvcir.2016.04.021

Cited by 5 publications

(5 citation statements)

References 36 publications

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“…In terms of BER, the proposed method with quadrants (4 pieces), halves (2 pieces), and single-piece achieved 89.6%, 87.9%, and 80.44% in image retrieval. The highest score belongs to AIR+LCDP+TSR [30] with 100% BER however, this study has two stages in which they eliminate unrelated images classed in the first one. Therefore they could achieve very high accuracy.…”

Section: Resultsmentioning

confidence: 99%

Quadrant-based contour features for accelerated shape retrieval system

Yıldırım

2022

Journal of Electrical Engineering

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Shape representation and retrieval are essential research topics of computer vision. This paper proposes a novel feature set to be used in content-based image retrieval systems. The proposed method is an extended version of our previous study which uses contour information of shapes. The previous study calculated the center of mass (CoM) of the shape. By taking the CoM as origin, we created imaginary vectors in every angular direction. From each vector, we extracted three features which are the number of intersections between vector and contour, average distance of intersection points to CoM, and standard deviation of these points. In this method, we extract novel features and decrease the size of the feature set to decrease the computation time. We divide the shape into quadrants and represent each quadrant by nine features. Each shape image is represented by a 4x9 feature vector. We tested the proposed method on MPEG-7 and ETH-80 datasets and compared it with the state-of-art. According to the results, our method decreased the computation time dramatically while giving a state-of-art level retrieval accuracy.

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Section: Resultsmentioning

confidence: 99%

Quadrant-based contour features for accelerated shape retrieval system

Yıldırım

2022

Journal of Electrical Engineering

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“…To test its performance, we compared our method with the recent state-of-the-art. We demonstrate the retrieval and recognition performance of the proposed method by conducting experiments on three widely used shape datasets: MPEG-7 [29], Kimia99 [30] and ETH-80 [31]. All the datasets are composed of various shape categories.…”

Section: Resultsmentioning

confidence: 99%

“…Each class consists of 20 shapes with variations. The benchmarking metric for this dataset is the retrieval performance and is measured by bulls eye rating in which every shape is used as query and the number of similar images that belong to the same class were counted in the top 40 matches [29].…”

Section: Resultsmentioning

confidence: 99%

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Shape retrieval using angle-wise contour variance

Yıldırım

İnce

Salman

et al. 2021

Journal of Electrical Engineering

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In this study, we propose a geometric feature set for 2D shape retrieval. Conventional Hough feature gives the edge locations along with angle and creates Hough table if there are multiple intersections at borders. In this paper, a statistical way to represent the relation of repeating contours at each angle around the shape centroid is presented. The main contribution of this paper is to use the standard deviation of repeating contours. We calculate the angle between the shape centroid and each point on the contour. For each integer angle value, three features were extracted: the number of contour repetitions, the average distance of the points at that angle to the centroid, and the standard deviation of the points at the same angle. Thus, a 2D image was represented by a constant sized matrix, regardless of its size. In the case of similarity between two images, instead of merging features within a single expression, the algorithm picked the feature with the highest similarity rate for that comparison. We tested the proposed method on MPEG-7, Kimia99, ETH-80 datasets for a benchmark with the state-of-the-art. It outperformed most of the recent methods in terms of retrieval rate.

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“…With further study of implant design, we gradually realized that shape features can improve the accuracy and efficiency of retrieval (Pan et al, 2016;Wang et al, 2019). To do this, a novel design method based on bionic vein structure is proposed in this research.…”

Section: Introductionmentioning

confidence: 99%

Convenient design method for customized implants based on bionic vein structure features

Wang

Geng

et al. 2022

Front. Bioeng. Biotechnol.

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Matching implants to bones is crucial for customized orthopedic medicine. Existing methods for designing customized implants predominantly adopt the parameterized deformation method that uses a fragmented representation of semantic parameters. Such a representation cannot provide information integration management and therefore restricts the retrieval of information regarding implant features and the improvement of customized design efficiency. Therefore, this study proposes a rapid design method for customized implants based on bionic vein structure features. First, a bionic vein structure was designed to represent the implant type. Second, the bionic vein structure was represented by a digraph structure with morphological and dimensional features. Finally, the implant model was rapidly built by retrieving the sketch and other modeling operations. Common implants such as the T-shaped plate, L-shaped plate, clover plate, and femoral stem prosthesis were used as explanations or test cases. The experimental work shows that combining the traditional parametric deformation method with bionic vein structure features in our present method is flexible and efficient results, and can improve the efficiency of customized implant design.

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A two-stage shape retrieval (TSR) method with global and local features

Cited by 5 publications

References 36 publications

Quadrant-based contour features for accelerated shape retrieval system

Quadrant-based contour features for accelerated shape retrieval system

Shape retrieval using angle-wise contour variance

Convenient design method for customized implants based on bionic vein structure features

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