Shape Similarity Retrieval Research Articles

Quantifying shape similarity of female upper body silhouettes based on 2D images

PurposeThis study focused on how to quantify the similarities of body shape based on the front and side images, and a shape comprehensive index (ISC) of female upper body shape based on 2D images was proposed.Design/methodology/approachIn total, 190 young women were shot for front and side images, and 18 shape parameters were automatically extracted, including seven angles and 11 ratio parameters. The coefficient of variation method was used to assign different weights for related parameters, and the ISC was calculated to describe the body shape of each subject. Five cross-sectional curves of the upper body (e.g. shoulder, chest, waist, abdomen and hip) were selected for exploring the range of shape similarity.FindingsAccording to the value of ISC, if the difference among the subjects is within the range of ±0.02, their body shapes can be regarded as similar, and the subject with the minimum distance is considered as the most similar. Error results show that the error range of the angle parameter is from 0.2° to 3.6° and the ratio range is from 0.001 to 0.119. Moreover, the t-test value among the parameters of the similar body is above 0.05, indicating that there is no significant difference for the upper body shape of the similar groups.Originality/valueThis method can quantify body shapes with the upper body characteristics of young women instead of subjective judgment. The study can be extended to other parts of the body and can also provide a new thought for shape similarity retrieval based on 2D images.

Robust shape similarity retrieval based on contour segmentation polygonal multiresolution and elastic matching

In this paper, we are going to present a novel shape similarity retrieval algorithm that can be used to match and recognize 2D objects. The match process uses a new multi-resolution polygonal shape descriptor that is invariant to scale, rotation and translation. The shape descriptor equally segments the contour of any shape, regardless of its complexity, and captures three features around its center including the distance and slope relative to the center. All parameters are normalized relative to the max values. The novel shape matching algorithm uses the shape descriptor and applies it by linearly scanning a stored set of shapes and measuring the similarity using elastic comparisons of shape segments. Similarity measurement is achieved by the sum of differences distance measure. The multi-resolution segmentation provides flexibility for applications that have different time and space requirements while maintaining high accuracy results and the elastic matching adds an advantage when matching partially occluded shapes. We applied our algorithms on many test databases including the MPEG-7 shape core experiment and achieved the highest result reported with a score of 84.33% for the MPEG-7 Part B similarity test.

A learning-based approach to shape-similarity retrieval of 3D models

A learning-based approach to shape-similarity retrieval of 3D models

Measuring the Performance of Shape Similarity Retrieval Methods

When datamining large databases with images or drawings of objects, there is a need to search for objects with a shape similarity. According to recent overview papers the issue of comparing different shape similarity retrieval methods and systems has largely been neglected in the research community due to the subjective character of such comparisons. Indeed, the evaluation of a shape similarity retrieval system can only be made with reference to a particular application, in our case retrieval of drawings of aluminum sections. In this paper we propose five different new performance measures for shape similarity retrieval. We have applied the methods on six different feature sets including skeleton, moment, Fourier, and fuzzy/symmetry features. The results clearly show that shape similarity retrieval is both application and representation dependent and can be evaluated by a number of independent methods.

Curvature scale space image in shape similarity retrieval

In many applications, the user of an image database system points to an image, and wishes to retrieve similar images from the database. Computer vision researchers aim to capture image information in feature vectors which describe shape, texture and color properties of the image. These vectors are indexed or compared to one another during query processing to find images from the database. This paper is concerned with the problem of shape similarity retrieval in image databases. Curvature scale space (CSS) image representation along with a small number of global parameters are used for this purpose. The CSS image consists of several arch-shape contours representing the inflection points of the shape as it is smoothed. The maxima of these contours are used to represent a shape. The method is then tested on a database of 1100 images of marine creatures. A classified subset of this database is used to evaluate the method and compare it with other methods. The results show the promising performance of the method and its superiority over Fourier descriptors and moment invariants.