Time-frequency-tuned salient region detection and segmentation

Kulshreshtha, Akanksha; Deshpande, Abhinav V.; Meher, Saroj K.

doi:10.1109/iadcc.2013.6514377

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…As an extension of image saliency in the field of video, video saliency [16,17] has also received extensive attention. According to the features, the current video saliency detection methods can be divided into three categories: methods based on space domain [18], methods based on time domain [19,20] and methods based on spatiotemporal domain [21]. It is generally believed that image saliency represents the most salient region in the current image, and the region most likely to be labeled as foreground.…”

Section: Introductionmentioning

confidence: 99%

Strong target constrained video saliency detection

Han

Liu

2019

SN Appl. Sci.

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Aiming at the problem of video saliency detection, a strong target constrained video saliency detection method is proposed in this paper. In order to detect the salient region fast and effectively, strong target constraints forcing by the location, scale and color model are introduced into the video saliency detection. First, a target locating strategy for obtaining the location and scale information is proposed by correcting the result of video tracking with the optical flow result and the segmentation result of the last frame. Second, the estimated color model of the target is also calculated by the obtained segmentation results. Finally, the strong target constraints are integrated into the saliency model in the way of extending the significance hypothesis, and a high quality saliency map is obtained, where segmentation is employed for constrained parameters updating. In details, Densecut is initialized by the obtained saliency map to calculate the segmentation result of the last frame. Compared with some state-of-art saliency detection methods, our proposed method performs outstandingly, and the results on DAVIS dataset are significantly improved in terms of accuracy and robustness.

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Section: Introductionmentioning

confidence: 99%

Strong target constrained video saliency detection

Han

Liu

2019

SN Appl. Sci.

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A Unified Framework for Saliency Segmentation

Kakanopas

Woraratpanya

2021

Lecture Notes in Networks and Systems

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MLBSNet: Mutual Learning and Boosting Segmentation Network for RGB-D Salient Object Detection

Xia,

Wang,

2024

Electronics

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RGB-D saliency object detection (SOD) primarily segments the most salient objects from a given scene by fusing RGB images and depth maps. Due to the inherent noise in the original depth map, fusion failures may occur, leading to performance bottlenecks. To address this issue, this paper proposes a mutual learning and boosting segmentation network (MLBSNet) for RGB-D saliency object detection, which consists of a deep optimization module (DOM), a semantic alignment module (SAM), a cross-modal integration (CMI) module, and a separate reconstruct decoder (SRD). Specifically, the deep optimization module aims to obtain optimal depth information by learning the similarity between the original and predicted depth maps. To eliminate the uncertainty of single-modal neighboring features and capture the complementary features of multiple modalities, a semantic alignment module and a cross-modal integration module are introduced. Finally, a separate reconstruct decoder based on a multi-source feature integration mechanism is constructed to overcome the accuracy loss caused by segmentation. Through comparative experiments, our method outperforms 13 existing methods on five RGB-D datasets and achieves excellent performance on four evaluation metrics.

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Time-frequency-tuned salient region detection and segmentation

Cited by 3 publications

References 20 publications

Strong target constrained video saliency detection

Strong target constrained video saliency detection

A Unified Framework for Saliency Segmentation

MLBSNet: Mutual Learning and Boosting Segmentation Network for RGB-D Salient Object Detection

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