Salient Object Detection Techniques in Computer Vision—A Survey

Gupta, Ashish Kumar; Seal, Ayan; Prasad, Mukesh; Khanna, Pritee

doi:10.3390/e22101174

Cited by 68 publications

(33 citation statements)

References 228 publications

(526 reference statements)

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“…The SOD approach has gained more popularity than the EFP approach because of its ability to identify the essential characteristics of salient objects than predicting their locations only [56,72]. The salient regions are usually considered as perceptually distinct image parts that are dissimilar to their backgrounds [42]. The dissimilarity, rarity, or uniqueness has been extensively studied with several advancements in the bottom-up SOD approach [42,60].…”

Section: Bottom-up Saliency Detection Methodsmentioning

confidence: 99%

“…The approach has had great success in salient object detection with the progress of deep learning methods [8,[37][38][39][40][41]. Deep saliency detection methods are often trained with a large set of finely annotated pixel-level ground truth images [42][43][44]. However, the performance of deep learning methods is highly dependent on the construction of well-annotated training datasets and can be adversely affected [43,45].…”

Section: Introductionmentioning

confidence: 99%

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Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

Joseph

Olugbara

2021

J. Imaging

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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.

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Section: Bottom-up Saliency Detection Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

Joseph

Olugbara

2021

J. Imaging

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“…The attention module can be divided into spatial attention module, channel attention module, and mixed attention module according to the attention principle [ 48 , 49 , 50 ]. The task of spatial attention module is to find the 2D spatial position containing the information of interest in a single feature map.…”

Section: Related Workmentioning

confidence: 99%

Robust Vehicle Speed Measurement Based on Feature Information Fusion for Vehicle Multi-Characteristic Detection

Yang

Luo

Song

et al. 2021

Entropy

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A robust vehicle speed measurement system based on feature information fusion for vehicle multi-characteristic detection is proposed in this paper. A vehicle multi-characteristic dataset is constructed. With this dataset, seven CNN-based modern object detection algorithms are trained for vehicle multi-characteristic detection. The FPN-based YOLOv4 is selected as the best vehicle multi-characteristic detection algorithm, which applies feature information fusion of different scales with both rich high-level semantic information and detailed low-level location information. The YOLOv4 algorithm is improved by combing with the attention mechanism, in which the residual module in YOLOv4 is replaced by the ECA channel attention module with cross channel interaction. An improved ECA-YOLOv4 object detection algorithm based on both feature information fusion and cross channel interaction is proposed, which improves the performance of YOLOv4 for vehicle multi-characteristic detection and reduces the model parameter size and FLOPs as well. A multi-characteristic fused speed measurement system based on license plate, logo, and light is designed accordingly. The system performance is verified by experiments. The experimental results show that the speed measurement error rate of the proposed system meets the requirement of the China national standard GB/T 21555-2007 in which the speed measurement error rate should be less than 6%. The proposed system can efficiently enhance the vehicle speed measurement accuracy and effectively improve the vehicle speed measurement robustness.

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“…According to a recent survey on saliency estimation techniques [28], 751 research papers were published on this topic since 2008. Some of them employ conventional techniques including contrast, diffusion, backgroundness and objectness prior, low-rank matrix recovery, and Bayesian, etc, and others employ deep learning techniques including supervised, weakly supervised and adversarial methods.…”

Section: Review Of Saliency Estimation Schemesmentioning

confidence: 99%

A Saliency-Based Patch Sampling Approach for Deep Artistic Media Recognition

Yang

Min

2021

Electronics

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We present a saliency-based patch sampling strategy for recognizing artistic media from artwork images using a deep media recognition model, which is composed of several deep convolutional neural network-based recognition modules. The decisions from the individual modules are merged into the final decision of the model. To sample a suitable patch for the input of the module, we devise a strategy that samples patches with high probabilities of containing distinctive media stroke patterns for artistic media without distortion, as media stroke patterns are key for media recognition. We design this strategy by collecting human-selected ground truth patches and analyzing the distribution of the saliency values of the patches. From this analysis, we build a strategy that samples patches that have a high probability of containing media stroke patterns. We prove that our strategy shows best performance among the existing patch sampling strategies and that our strategy shows a consistent recognition and confusion pattern with the existing strategies.

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Salient Object Detection Techniques in Computer Vision—A Survey

Cited by 68 publications

References 228 publications

Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

Robust Vehicle Speed Measurement Based on Feature Information Fusion for Vehicle Multi-Characteristic Detection

A Saliency-Based Patch Sampling Approach for Deep Artistic Media Recognition

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