Adaptive fusion with multi-scale features for interactive image segmentation

Ding, Zongyuan; Wang, Tao; Sun, Quansen; Wang, Hongyuan

doi:10.1007/s10489-020-02114-3

Cited by 9 publications

(4 citation statements)

References 36 publications

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“…i. Strokes [15][16][17]: The user must apply stroke(s) to the image's object of interest and background. ii.…”

Section: Methodsmentioning

confidence: 99%

How Does Image Complexity Affect the Accuracy of an Interactive Image Segmentation Algorithm?

Kok Luong Goh,

Soo See Chai,

Muzaffar Hamzah

et al. 2024

ARASET

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This study investigates the impact of image complexity on the accuracy of interactive image segmentation algorithms. Image complexity plays a crucial role in segmentation performance, yet previous studies have primarily relied on subjective methods, leaving a gap in understanding how objective measures impact accuracy. The purpose of this research is to explore the relationship between image complexity and segmentation performance and to propose an adaptive approach for improving accuracy based on complexity measures. The study utilizes objective measures, namely entropy and fractal dimension, to quantify image complexity. An interactive image segmentation algorithm is employed, with a bounding box as the background and strokes as the foreground annotations. The number of strokes is dynamically adjusted based on complexity measures, ensuring a tailored segmentation approach. Comparative evaluations are conducted to assess the effectiveness of dynamic and fixed stroke allocation strategies. The principal results reveal a significant influence of image complexity on segmentation accuracy. The dynamic stroke allocation strategy outperforms fixed allocation, highlighting the importance of adapting to complexity. Moreover, the optimal combination of strokes and superpixel sizes is explored, providing valuable insights for practitioners. The findings emphasize the need to consider image complexity in segmentation algorithms to achieve accurate results. In conclusion, this study contributes to the understanding of the relationship between image complexity and interactive image segmentation. By introducing a dynamic stroke allocation approach and evaluating different configurations, the research provides insights into optimizing accuracy based on image complexity. The adaptive strategy improves segmentation performance and guides the development of robust algorithms. Future research can further refine the adaptive approach, explore additional complexity measures, and incorporate advanced machine learning techniques to enhance interactive image segmentation. Overall, this study advances the field by highlighting the importance of image complexity, providing guidance for practitioners, and paving the way for more efficient segmentation algorithms.

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“…i. Strokes [15][16][17]: The user must apply stroke(s) to the image's object of interest and background. ii.…”

Section: Methodsmentioning

confidence: 99%

How Does Image Complexity Affect the Accuracy of an Interactive Image Segmentation Algorithm?

Kok Luong Goh,

Soo See Chai,

Muzaffar Hamzah

et al. 2024

ARASET

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“…However, in order to accurately improve the features of crops, it's important that we better segment and effectively distinguish different crops in the image. In spite of good results in image segmentation through segmentation algorithms at multiple scales, there are still some phenomena such as misclassification, absent classification and loss of detail caused by low image resolution [74,75], and only single-cropping crops in the study area are classified. This study proposes to integrate the new crop edge profile information obtained by the Canny Edge Detection Algorithm into the segmentation process.…”

Section: Evaluation Of the Effect Of Combining Scale Segmentation Wit...mentioning

confidence: 99%

Study on Object-Based High-Resolution Remote Sensing Image Classification of Crop Planting Structure in the Loess Plateau of Eastern Gansu Province

Yang,

Qi,

Zhang

et al. 2024

Preprint

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Timely and accurate acquisition of information on distribution of the crop planting structure in Loess Plateau of eastern Gansu Province, one of the most important agricultural areas in western China, is crucial for promoting fine management of agriculture and ensuring food security. In this study, the Object-Based Image Classification (OBIC), the Random Forest (RF) and Convolutional Neural Network (CNN) models were employed to classify the crop planting structure of four representative test areas in Qingyang City in a precise manner. Firstly, different optimal segmentation scales for various crops were selected using the Estimation of Scale Parameter 2 (ESP2) tool and the Ratio of Mean Difference to Neighbors(ABS) to Standard Deviation (RMAS)model. The images were then segmented through multiresolution segmentation combined with the Canny Edge Detection algorithm. Secondly, the L1 regularized logistic regression model was utilized to select and optimize 39 spatial feature factors including spectral, textural, geometric, and index features, in conjunction with phenological factors. Lastly, under the multi-level classification framework, the Random Forest (RF) classifier and Convolutional Neural Network (CNN) model, combined with object-based multiresolution segmentation, was used to classify the crop planting structure. The findings show that: Thanks to the Canny Edge Detection algorithm, we can obtain a more complete boundary of the segmented objects and improve the separability. The optimal segmentation scales for corn, vegetables, and buckwheat were found to be 55, 70, and 35, respectively, while wheat and apple had optimal segmentation scales of 65. In addition to phenological characteristics, the number of selected spatial features for corn, vegetables, buckwheat, wheat, and apple were 9, 7, 16, 12, and 10, respectively. The CNN model demonstrated high consistency with the RF model in the classification results, but the accuracy of RF model is higher than that of CNN model on the whole. The overall accuracy of classification using RF model in four test areas registered 91.93%, 94.92%, 89.37% and 90.68%, respectively. This paper introduced crop phenological factors, effectively improving the extraction precision of shattered agricultural planting structure in the Loess Plateau of eastern Gansu Province. Its findings have important application value in crop monitoring, management, food security and other related fields.

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“… Strokes [20][21][22][23][24]: The user must apply stroke(s) to the image's object of interest and background.…”

Section: Literature Reviewmentioning

confidence: 99%

Superpixel Sizes using Topology Preserved Regular Superpixel Algorithm and their Impacts on Interactive Segmentation

Goh¹,

Chai²,

Ng³

et al. 2022

IJACSA

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Interactive Image Segmentation is a type of semiautomated segmentation that uses user input to extract the object of interest. It is possible to speed up and improve the end result of segmentation by using pre-processing steps. The use of superpixels is an example of a pre-processing step. A superpixel is a collection of pixels with similar properties such as texture and colour. Previous research was conducted to assess the impact of the number of superpixels (based on SEEDS superpixel aglorithms) required to achieve the best segmentation results. The study, however, only examined one type of input (strokes) and a small number of images. As a result, the goal of this study is to extend previous work by performing interactive segmentation with input strokes and a combination of bounding box and strokes on images from Grabcut image data sets generated by Topology preserved regular superpixel (TPRS). Based on our findings, an image with 1000 to 2500 superpixels and a combination of bounding box and strokes will help the interactive segmentation algorithm produce a good segmentation result. Finally, the size of the superpixels would influence the final segmentation results as well as the input type.

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Adaptive fusion with multi-scale features for interactive image segmentation

Cited by 9 publications

References 36 publications

How Does Image Complexity Affect the Accuracy of an Interactive Image Segmentation Algorithm?

How Does Image Complexity Affect the Accuracy of an Interactive Image Segmentation Algorithm?

Study on Object-Based High-Resolution Remote Sensing Image Classification of Crop Planting Structure in the Loess Plateau of Eastern Gansu Province

Superpixel Sizes using Topology Preserved Regular Superpixel Algorithm and their Impacts on Interactive Segmentation

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