Combining multi-layer integration algorithm with background prior and label propagation for saliency detection

Xia, Chenxing; Zhang, Hanling; Gao, Xiuju

doi:10.1016/j.jvcir.2017.06.009

Cited by 7 publications

(2 citation statements)

References 26 publications

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“…In [20], a center-prior is exploited to calculate the distribution of background parts and the part saliency is defined by weighting the color contrast. Similarly, a background-prior map is constructed based on some corner cues in [21] and corresponding regions of interest are calculated by reverse-measurement principle. Furthermore, the convex hull of feature points is utilized to predict foreground elements in [22] and then a smooth process is performed by minimizing a energy function.…”

Section: Introductionmentioning

confidence: 99%

Salient Ship Detection via Background Prior and Foreground Constraint in Remote Sensing Images

Zhi

Zhang

et al. 2020

Remote Sensing

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Automatic ship detection in complicated maritime background is a challenging task in the field of optical remote sensing image interpretation and analysis. In this paper, we propose a novel and reliable ship detection framework based on a visual saliency model, which can efficiently detect multiple targets of different scales in complex scenes with sea clutter, clouds, wake and islands interferences. Firstly, we present a reliable background prior extraction method adaptive for the random locations of targets by computing boundary probability and then generate a saliency map based on the background prior. Secondly, we compute the prior probability of salient foreground regions and propose a weighting function to constrain false foreground clutter, gaining the foreground-based prediction map. Thirdly, we integrate the two prediction maps and improve the details of the integrated map by a guided filter function and a wake adjustment function, obtaining the fine selection of candidate regions. Afterwards, a classification is further performed to reduce false alarms and produce the final ship detection results. Qualitative and quantitative evaluations on two public available datasets demonstrate the robustness and efficiency of the proposed method against four advanced baseline methods.

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Section: Introductionmentioning

confidence: 99%

Salient Ship Detection via Background Prior and Foreground Constraint in Remote Sensing Images

Zhi

Zhang

et al. 2020

Remote Sensing

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“…I MAGE-text search has attracted much attention due to the explosive growth of data in search engines and social networks in recent years. Image-text search plays an important role in many scenarios in the fields of target monitoring and object tracking [1]- [3], video surveillance [4], [5], audio-text recognition [6], face and saliency detection [7], [8], human computer interaction [9], [10] and multimodal modelling [11], [12], etc. Given an image query, the task of image-text search is to retrieve the most relevant texts in a text dataset, and vice versa.…”

Section: Introductionmentioning

confidence: 99%

Supervised Matrix Factorization Hashing With Quantitative Loss for Image-Text Search

et al. 2020

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Image-text hashing approaches have been widely applied in large-scale similarity search applications due to their efficiency in both search speed and storage efficiency. Most recent supervised hashing approaches learn a hash function by constructing a pairwise similarity matrix or directly learning the hash function and hash code (i.e.,1 or-1) procedure based on class labels. However, the former suffers from high training complexity and storage cost, and the latter ignores the semantic correlation of the original data, both of which prevent discriminative hash codes. To this end, we propose a novel discrete hashing algorithm called supervised matrix factorization hashing with quantitative loss (SMFH-QL). The proposed SMFH-QL first generates hash codes via the class label, avoiding the construction of a pairwise similarity; then, matrix factorization is used to design hash codes from original image-text data, thereby eliminating the impact of class labels and reducing the quantization error. Moreover, we introduce a quantitative loss function term to learn hash codes by incorporating class labels and the original data information, facilitating learning a similarity-preserving hash function in image-text search. Extensive experiments show that SMFH-QL outperforms several existing hashing methods on three representative datasets. INDEX TERMS Image-text search, supervised hashing, hash function, hash codes, quantitative loss function.

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Attentive Semantic and Perceptual Faces Completion Using Self-attention Generative Adversarial Networks

Liu

2019

Neural Process Lett

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Combining multi-layer integration algorithm with background prior and label propagation for saliency detection

Cited by 7 publications

References 26 publications

Salient Ship Detection via Background Prior and Foreground Constraint in Remote Sensing Images

Salient Ship Detection via Background Prior and Foreground Constraint in Remote Sensing Images

Supervised Matrix Factorization Hashing With Quantitative Loss for Image-Text Search

Attentive Semantic and Perceptual Faces Completion Using Self-attention Generative Adversarial Networks

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