Computing, explaining and visualizing shape similarity in content-based image retrieval

Andreou, Ioannis; Sgouros, Nikitas M.

doi:10.1016/j.ipm.2004.08.008

Cited by 14 publications

(7 citation statements)

References 10 publications

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“…Pixel-based metrics can extract contours and features from these images. Another approach is to use shape similarity [ 18 ] and shape matching [ 19 ]. This way, we can use standard geometrical objects, such as circles and squares for which contraction prediction is less complicated.…”

Section: Discussion and Further Workmentioning

confidence: 99%

A Bayesian finite-element trained machine learning approach for predicting post-burn contraction

Egberts

Schaaphok

Vermolen

et al. 2022

Neural Comput & Applic

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Burn injuries can decrease the quality of life of a patient tremendously, because of esthetic reasons and because of contractions that result from them. In severe case, skin contraction takes place at such a large extent that joint mobility of a patient is significantly inhibited. In these cases, one refers to a contracture. In order to predict the evolution of post-wounding skin, several mathematical model frameworks have been set up. These frameworks are based on complicated systems of partial differential equations that need finite element-like discretizations for the approximation of the solution. Since these computational frameworks can be expensive in terms of computation time and resources, we study the applicability of neural networks to reproduce the finite element results. Our neural network is able to simulate the evolution of skin in terms of contraction for over one year. The simulations are based on 25 input parameters that are characteristic for the patient and the injury. One of such input parameters is the stiffness of the skin. The neural network results have yielded an average goodness of fit ($$R^2$$ R 2 ) of 0.9928 (± 0.0013). Further, a tremendous speed-up of 19354X was obtained with the neural network. We illustrate the applicability by an online medical App that takes into account the age of the patient and the length of the burn.

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Section: Discussion and Further Workmentioning

confidence: 99%

A Bayesian finite-element trained machine learning approach for predicting post-burn contraction

Egberts

Schaaphok

Vermolen

et al. 2022

Neural Comput & Applic

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“…Computer vision algorithms identify the similarity of two image objects through the lens of "shape representation" or "shape matching." Generally, such algorithms may be used for database retrieval or image object retrieval [51]- [54]. Shape matching algorithms are not typically spatially explicit and instead focus on identifying patterns regardless of size or orientation [55], [56].…”

Section: B Metric Calculation 1) Ee Computationmentioning

confidence: 99%

Assessing the Shape Accuracy of Coarse Resolution Burned Area Identifications

Humber

Boschetti

Giglio

2020

IEEE Trans. Geosci. Remote Sensing

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Accuracy assessment of burned area maps has been traditionally performed using pixel-based metrics, with the objective of assessing the accuracy and precision of burned area estimates at local and regional scales. While these assessments are helpful for obtaining consistent estimates of the burned area across many fires and over large areas, pixel-based approaches do not necessarily characterize how well individual fires are mapped. At the individual fire scale, other factors like the shape of the fire have significance regarding ecology, fire succession, and landscape management and determining other fire properties such as the spread rate. We propose a method for evaluating wildfire classification maps, which retains the spatially explicit properties of the burn scar. Our method quantifies the edge error (EE) of burned area classifications and reference maps by calculating the average geometric normal of the evaluated burned area boundary along the burn edge and the two nearest neighbor samples from the reference burn boundary. The metric is a physically meaningful quantification of the EE, which represents the average distance between the boundaries of the reference and evaluated burn scars. The methods are demonstrated by comparing MODIS Burned Area (MCD64A1) maps to Monitoring Trends in Burn Severity (MTBS) maps for 173 total wildfires in the United States.The results indicate that when accounting for the minimum achievable EE (MAEE) due to differing spatial resolutions, the mean EE is less than two MODIS pixels and the magnitude of the errors does not appear to be related to fire size.

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“…Shape matching has been approached in various ways. A few of the frequently applied techniques are: tree pruning, the generalized Hough transform, geometric hashing, the alignment method, various statistics, deformable templates, relaxation labeling, Fourier and wavelet transforms, curvature scale space, and classifiers such as neural networks [12].…”

Section: Shape Matchingmentioning

confidence: 99%

“…Recently, Andreou and Sgouros [12] discussed their: "turning function difference", as a part of their G Computer Vision library. It is an efficient and effective shape matching method.…”

Section: Shape Matchingmentioning

confidence: 99%

Human-Centered Object-Based Image Retrieval

Broek

Rikxoort

Schouten

2005

Pattern Recognition and Image Analysis

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A new object-based image retrieval (OBIR) scheme is introduced. The images are analyzed using the recently developed, human-based 11 colors quantization scheme and the color correlogram. Their output served as input for the image segmentation algorithm: agglomerative merging, which is extended to color images. From the resulting coarse segments, boundaries are extracted by pixelwise classification, which are smoothed by erosion and dilation operators. The resulting features of the extracted shapes, completed the data for a -vector. Combined with the intersection distance measure, this vector is used for OBIR, as are its components. Although shape matching by itself provides good results, the complete vector outperforms its components, with up to 80% precision. Hence, a unique, excellently performing, fast, on human perception based, OBIR scheme is achieved.

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Computing, explaining and visualizing shape similarity in content-based image retrieval

Cited by 14 publications

References 10 publications

A Bayesian finite-element trained machine learning approach for predicting post-burn contraction

A Bayesian finite-element trained machine learning approach for predicting post-burn contraction

Assessing the Shape Accuracy of Coarse Resolution Burned Area Identifications

Human-Centered Object-Based Image Retrieval

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