Salient Object Detection: Integrate Salient Features in the Deep Learning Framework

Chen, Qixin; Li, Shipeng; Shang, Yuanyuan; Shao, Zhuhong; Ding, Hui

doi:10.1109/access.2019.2948062

Cited by 13 publications

(5 citation statements)

References 36 publications

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“…For example, MINet [17] is a network that fuses features of adjacent layers, which detects salient objects through a strategy to minimize noise caused by the difference in resolution of feature maps by using small up-/down-sampling rates. Chen et al [18] proposes a novel method with designing a parallel multi-scale structure to integrate the salient features at each levels. Ji et al [19] proposed a method for learning context between feature information of different scales by applying spatial and channel unit attention modules to multi-scale encoder-decoder networks.…”

Section: Related Workmentioning

confidence: 99%

Data and Model Uncertainty Aware Salient Object Detection

Lee,

Song

2024

IEEE Access

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In general, salient object detection (SOD) datasets have ambiguity due to annotation accuracy and human subjectivity in determining saliency. Since this data uncertainty causes inaccurate prediction, many techniques tackling data uncertainty have been proposed so far. Previous works estimated data uncertainty in terms of predictive inaccuracy and adjusted the learning contribution so that a given model can focus more on specific data. However, inaccurate predictions can occur due to not only data uncertainty but also model uncertainty in which the model does not fully explain the data. As a result, a region that is inaccurately predicted due to model uncertainty is considered a region with high data uncertainty, resulting in insufficient learning. To solve this problem, we propose a novel uncertainty-aware learning scheme where model uncertainty is decomposed from prediction uncertainty and it is minimized. Also, we propose a refinement method to further improve performance by correcting the prediction result using data uncertainty in the inference step. The proposed uncertainty-aware method excludes data uncertainty from learning step and inference step more effectively, making the model more accurately detect salient object(s). The experimental results prove that the proposed method achieves state-of-the-art performance on several SOD datasets and qualitatively detects salient objects more accurately than the prior arts. The code will be uploaded on Github.

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Section: Related Workmentioning

confidence: 99%

Data and Model Uncertainty Aware Salient Object Detection

Lee,

Song

2024

IEEE Access

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“…Few modifications are required to boost the efficiency of image processing while dealing with denoising challenges. A graph convolution layer can be added into a trainable neural network design [43][44][45][46], which discovers the relationship between the network's hidden features, hence enhancing the network's robust learning power. Each pixel is represented as a vertex in a graph convolution network, and dynamically determined similarities are represented as edges.…”

Section: Object Detection Using Graph Based Networkmentioning

confidence: 99%

Realtime Object Detection Through M-ResNet in Video Surveillance System

Prabu¹,

Gnanasekar²

2023

Intelligent Automation &Amp; Soft Computing

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Object detection plays a vital role in the video surveillance systems. To enhance security, surveillance cameras are now installed in public areas such as traffic signals, roadways, retail malls, train stations, and banks. However, monitoring the video continually at a quicker pace is a challenging job. As a consequence, security cameras are useless and need human monitoring. The primary difficulty with video surveillance is identifying abnormalities such as thefts, accidents, crimes, or other unlawful actions. The anomalous action does not occur at a higher rate than usual occurrences. To detect the object in a video, first we analyze the images pixel by pixel. In digital image processing, segmentation is the process of segregating the individual image parts into pixels. The performance of segmentation is affected by irregular illumination and/or low illumination. These factors highly affect the real-time object detection process in the video surveillance system. In this paper, a modified ResNet model (M-Resnet) is proposed to enhance the image which is affected by insufficient light. Experimental results provide the comparison of existing method output and modification architecture of the ResNet model shows the considerable amount improvement in detection objects in the video stream. The proposed model shows better results in the metrics like precision, recall, pixel accuracy, etc., and finds a reasonable improvement in the object detection.

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“…Pang et al [17] dealt with multiscale features more effectively by applying an aggregate interaction module (AIM), which combines features from neighboring levels, and a self-interaction module (SIM), which makes effective use of intralayer features obtained through an AIM. Chen et al [18] proposed a network structure that integrates salient features for object detection.…”

Section: Related Work a Salient Object Detectionmentioning

confidence: 99%

Important Region Estimation Using Image Captioning

et al. 2022

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When storing images and videos on a limited storage device or transmitting them over a narrow-band network, an effective approach is to detect the necessary parts and process them preferentially. Visual saliency has often been used for this purpose, and many methods have been proposed to detect salient objects. However, a salient object is not necessarily the primary subject in an image. Determining the important regions in an image is not clear or easy to achieve because it generally depends on the context of the image. In this study, we propose a novel framework for detecting important image regions. We leverage an image-captioning technique because it interprets the context of an image when generating sentences. The proposed method determines those important regions that are closer to the level of human sensitivity by exploiting semantic information from the image captioning. To evaluate the effectiveness of the proposed method, we created a dataset that defines important regions within images based on experiments using subjective evaluation. Applying this dataset, we confirmed that the accuracy of the proposed approach was higher than that of conventional saliency-based object detection methods.

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Salient Object Detection: Integrate Salient Features in the Deep Learning Framework

Cited by 13 publications

References 36 publications

Data and Model Uncertainty Aware Salient Object Detection

Data and Model Uncertainty Aware Salient Object Detection

Realtime Object Detection Through M-ResNet in Video Surveillance System

Important Region Estimation Using Image Captioning

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