Multi-scale salient object detection using graph ranking and global–local saliency refinement

Filali, Idir; Állili, Madjid; Benblidia, Nadjia

doi:10.1016/j.image.2016.07.007

Cited by 23 publications

(13 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The impact of superpixels granularity on the performance of saliency detection was demonstrated in [68]. Hence, the accuracy of saliency detection is highly dependent on the optimal selection of the superpixels granularity [105]. Determining an optimal superpixels granularity is a difficult task because of the diverse image categories.…”

Section: Region-based Saliency Detectionmentioning

confidence: 99%

Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

Joseph

Olugbara

2021

J. Imaging

View full text Add to dashboard Cite

Salient object detection represents a novel preprocessing stage of many practical image applications in the discipline of computer vision. Saliency detection is generally a complex process to copycat the human vision system in the processing of color images. It is a convoluted process because of the existence of countless properties inherent in color images that can hamper performance. Due to diversified color image properties, a method that is appropriate for one category of images may not necessarily be suitable for others. The selection of image abstraction is a decisive preprocessing step in saliency computation and region-based image abstraction has become popular because of its computational efficiency and robustness. However, the performances of the existing region-based salient object detection methods are extremely hooked on the selection of an optimal region granularity. The incorrect selection of region granularity is potentially prone to under- or over-segmentation of color images, which can lead to a non-uniform highlighting of salient objects. In this study, the method of color histogram clustering was utilized to automatically determine suitable homogenous regions in an image. Region saliency score was computed as a function of color contrast, contrast ratio, spatial feature, and center prior. Morphological operations were ultimately performed to eliminate the undesirable artifacts that may be present at the saliency detection stage. Thus, we have introduced a novel, simple, robust, and computationally efficient color histogram clustering method that agglutinates color contrast, contrast ratio, spatial feature, and center prior for detecting salient objects in color images. Experimental validation with different categories of images selected from eight benchmarked corpora has indicated that the proposed method outperforms 30 bottom-up non-deep learning and seven top-down deep learning salient object detection methods based on the standard performance metrics.

show abstract

Section: Region-based Saliency Detectionmentioning

confidence: 99%

Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

Joseph

Olugbara

2021

J. Imaging

View full text Add to dashboard Cite

show abstract

“…In the second stage, the foreground nodes obtained from adaptive thresholding of the inverted initial saliency map act as the salient queries and the manifold ranking is re-applied to compute the final saliency scores for each superpixel. Filali et al [ 113 ] have extended the formulation single-layer manifold ranking framework to multi-layer saliency graphs, and utilized texture cues along with color to more accurately detect the boundaries of salient objects.…”

Section: Conventional Salient Object Detectionmentioning

confidence: 99%

Salient Object Detection Techniques in Computer Vision—A Survey

Gupta

Seal

Prasad

et al. 2020

Entropy

View full text Add to dashboard Cite

Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end.

show abstract

“…graph-based and generative methods (Van Engelen and Hoos 2019). Graph-based methods have demonstrated a great performance to separate classes with a manifold structures thanks to their ability to efficiently encode relational information among samples (Filali, Allili, and Nadjia 2016). However, given their transductive nature, they are not generalizable to classifying new data.…”

Section: Introductionmentioning

confidence: 99%

“…For example, in social networks, groups can be constructed by forming strong communities. Likewise, superpixel groups can be formed in image/video segmentation using spatially/temporally contiguous pixels (Filali, Allili, and Nadjia 2016).…”

Section: Introductionmentioning

confidence: 99%

Weakly Semi Supervised learning based Mixture Model With Two-Level Constraints

Nouboukpo

Állili

2021

FLAIRS

Self Cite

View full text Add to dashboard Cite

We propose a new weakly supervised approach for classification and clustering based on mixture models. Ourapproach integrates multi-level pairwise group and classconstraints between samples to learn the underlyinggroup structure of the data and propagate (scarce) initial labels to unlabelled data. Our algorithm assumes thenumber of classes is known but does not assume anyprior knowledge about the number of mixture components in each class. Therefore, our model : (1) allocatesmultiple mixture components to individual classes, (2)estimates automatically the number of components ofeach class, 3) propagates class labels to unlabelled datain a consistent way to predefined constraints. Experiments on several real-world and synthetic data datasetsshow the robustness and performance of our model overstate-of-the-art methods.

show abstract

Multi-scale salient object detection using graph ranking and global–local saliency refinement

Cited by 23 publications

References 70 publications

Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

Salient Object Detection Techniques in Computer Vision—A Survey

Weakly Semi Supervised learning based Mixture Model With Two-Level Constraints

Contact Info

Product

Resources

About