A Local Texture-Based Superpixel Feature Coding for Saliency Detection Combined with Global Saliency

Nan, Bingfei; Zhang, Mu; Chen, Long; Cheng, Jian

doi:10.3390/app5041528

Cited by 4 publications

(5 citation statements)

References 24 publications

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“…For example, Cheng et al [15] proposed a global contrast method using the histogram of pixel color to construct region contrast. In literature [24], local texture patterns, color distribution, and contour information were combined to encode superpixels for region contrast computing. In addition to handcrafted features, heuristic priors are also often employed for saliency detection.…”

Section: Related Workmentioning

confidence: 99%

Improved U-Net-Like Network for Visual Saliency Detection Based on Pyramid Feature Attention

Gong

Qingge

Liu

et al. 2022

Wireless Communications and Mobile Computing

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As a widely used technology, visual saliency detection has attracted a lot of attention in the past decades. Although a large number of methods, especially fully convolutional neural network- (FCN-) based approaches, have been proposed and achieved remarkable performance, it is still of great value to extend representative architecture to visual saliency detection task. In this paper, we propose an improved U-Net-like network, pyramid feature attention-based U-Net-like (PFAU-Net) for visual saliency detection problem. The main improvements of the proposed model include that in order to enable the network to extract features with more representation ability, we introduce a context-aware feature extraction (CFE) module and a channel attention module into the U-shaped backbone to obtain valuable multiscale features, and a feature pyramid path is also utilized in the decoder part of the network to take advantages of multilevel information. Moreover, we construct the loss function using three terms including pixel-level cross-entropy, image-level intersection over union (IoU), and a structural similarity term, which aim to make the model learn more saliency related knowledge. To verify the effectiveness of the proposed model, we conduct extensive experiments on six widely used public datasets, and the experimental results indicate that (1) our improved model can significantly improve the performance of the backbone network on all test datasets, and (2) our proposed model can outperform comparison FCN-based networks and nonneural network approaches. Both objective and qualitative evaluations verify the effectiveness of our proposed model.

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Section: Related Workmentioning

confidence: 99%

Improved U-Net-Like Network for Visual Saliency Detection Based on Pyramid Feature Attention

Gong

Qingge

Liu

et al. 2022

Wireless Communications and Mobile Computing

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“…Feature extraction and selection enable representation of the information present in the image, and limit the number of features, thus allowing further analysis within a reasonable time. Feature extraction was used in a wide range of applications, such as biometrics [12,14,15], classification of cloth, surfaces, landscapes, wood, and rock minerals [16,17], saliency detection [18], and background subtraction [19], among others. During the past 40 years, while a substantial number of methods for grayscale texture classification were developed [3,5], there was also a growing interest in colored textures [1,2,9,10,13,20,21].…”

Section: Introductionmentioning

confidence: 99%

Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing

Navarro-Clavería¹

2019

Applied Sciences

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Many applications in image analysis require the accurate classification of complex patterns including both color and texture, e.g., in content image retrieval, biometrics, and the inspection of fabrics, wood, steel, ceramics, and fruits, among others. A new method for pattern classification using both color and texture information is proposed in this paper. The proposed method includes the following steps: division of each image into global and local samples, texture and color feature extraction from samples using a Haralick statistics and binary quaternion-moment-preserving method, a classification stage using support vector machine, and a final stage of post-processing employing a bagging ensemble. One of the main contributions of this method is the image partition, allowing image representation into global and local features. This partition captures most of the information present in the image for colored texture classification allowing improved results. The proposed method was tested on four databases extensively used in color–texture classification: the Brodatz, VisTex, Outex, and KTH-TIPS2b databases, yielding correct classification rates of 97.63%, 97.13%, 90.78%, and 92.90%, respectively. The use of the post-processing stage improved those results to 99.88%, 100%, 98.97%, and 95.75%, respectively. We compared our results to the best previously published results on the same databases finding significant improvements in all cases.

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“…Researchers have always pursued high-dynamic-range imaging (HDRI) of the remote sensing camera, regardless of using CCDs or CMOSs as the image sensors [6][7][8][9][10][11][12]. The reason is that the From the perspective of definition, to improve the imaging dynamic range of the camera, the direct measures can be divided into two aspects.…”

Section: Introductionmentioning

confidence: 99%

“…One aspect is based on increasing the full well capacity of the pixels, and the other is to reduce the noise. However, restricted by the current manufacturing level of the CMOS, it is difficult for these two parameters to meet higher requirements [7]. Therefore, we must explore other methods beyond the sensor itself.…”

Section: Introductionmentioning

confidence: 99%

A High-Dynamic-Range Optical Remote Sensing Imaging Method for Digital TDI CMOS

Lan

Xue

et al. 2017

Applied Sciences

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Abstract:The digital time delay integration (digital TDI) technology of the complementary metal-oxide-semiconductor (CMOS) image sensor has been widely adopted and developed in the optical remote sensing field. However, the details of targets that have low illumination or low contrast in scenarios of high contrast are often drowned out because of the superposition of multi-stage images in digital domain multiplies the read noise and the dark noise, thus limiting the imaging dynamic range. Through an in-depth analysis of the information transfer model of digital TDI, this paper attempts to explore effective ways to overcome this issue. Based on the evaluation and analysis of multi-stage images, the entropy-maximized adaptive histogram equalization (EMAHE) algorithm is proposed to improve the ability of images to express the details of dark or low-contrast targets. Furthermore, in this paper, an image fusion method is utilized based on gradient pyramid decomposition and entropy weighting of different TDI stage images, which can improve the detection ability of the digital TDI CMOS for complex scenes with high contrast, and obtain images that are suitable for recognition by the human eye. The experimental results show that the proposed methods can effectively improve the high-dynamic-range imaging (HDRI) capability of the digital TDI CMOS. The obtained images have greater entropy and average gradients.

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A Local Texture-Based Superpixel Feature Coding for Saliency Detection Combined with Global Saliency

Cited by 4 publications

References 24 publications

Improved U-Net-Like Network for Visual Saliency Detection Based on Pyramid Feature Attention

Improved U-Net-Like Network for Visual Saliency Detection Based on Pyramid Feature Attention

Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing

A High-Dynamic-Range Optical Remote Sensing Imaging Method for Digital TDI CMOS

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