Textual Image Compression for Maintaining or Improving the Recognition Performance

Grailu, Hadi

doi:10.1007/s00034-016-0317-4

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…Hot region selection (HRS) technology can distinguish the importance degree of different regions and can further enable to highlight the important content of the images, and remove redundant information. The analysis research of HRS plays an important role in image compression and coding, image retrieval, target detection and recognition, scene analysis [5][6][7], active vision, and other fields. For example, the amount of data has a high requirement for the key diagnosis area in medical image processing [8], and the extraction of an area of interest can more easily locate the critical areas to reduce redundancy.…”

Section: Introductionmentioning

confidence: 99%

Hot Region Selection Based on Selective Search and Modified Fuzzy C-Means in Remote Sensing Images

Yin

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hot region selection (HRS) is usually performed for larger scenes, especially in military target detection and battlefield situational awareness applications. Many state-of-the-art methods pay more attention on object detection. Although they can detect the region of interest in one image, these images are highly affected by illumination, rotation, and scale changes, which further increases the complexity of analysis compared to those obtained using standard remote sensing platforms. The study of HRS is of great importance in the analysis of remote sensing images. The current research focuses on the specific type of object area detection. A well-developed HRS needs to have three properties: uniform highlighting of the entire HRS, well-defined boundaries, and good robustness. Motivated by these requirements, a hot region selection method based on the selective search method and modified fuzzy c-means in remote sensing images is proposed to address the detection of potential hot regions in large-scale remote sensing images. First, we create a Gaussian curvature filter to preprocess large scale remote sensing images. Second, a modified fuzzy c-means segmentation method is utilized to segment the image. Third, an enhanced selective search method is adopted to establish well-defined boundaries for the HRS and to improve the immunity to noise. The geographic information is presented in this phase, which is conducive to improve the detection accuracy. In the experimental section, we compare our new method with four other extraction models on three data sets. The experimental results show that compared to the other competing models, the new model better defines the hot regions and obtains more entire boundaries in terms of Overlap and mAP.

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Section: Introductionmentioning

confidence: 99%

Hot Region Selection Based on Selective Search and Modified Fuzzy C-Means in Remote Sensing Images

Yin

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

Textual Image Compression for Maintaining or Improving the Recognition Performance

Cited by 1 publication

References 17 publications

Hot Region Selection Based on Selective Search and Modified Fuzzy C-Means in Remote Sensing Images

Hot Region Selection Based on Selective Search and Modified Fuzzy C-Means in Remote Sensing Images

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