An efficient visual saliency detection model based on Ripplet transform

Due to complex lighting circumstances and other factors, visual saliency recognition of underwater images is not as good as visual saliency detection models that function well on land when applied to underwater environments. The dearth of large-scale annotated underwater picture datasets has further hindered the development of visual saliency detection in underwater images. we propose an attention-based approach for underwater image saliency detection to address this issue. The backbone network is pre-trained using both land and underwater image datasets, which effectively solves the problem of fewer datasets. After that, a residual refinement module incorporating a channel-space attention mechanism is merged, the model as a whole is trained end-to-end using underwater image datasets, and the model accuracy is experimentally tested. The experimental results in three underwater image datasets show that our method is better than several other methods in the mainstream evaluation indicators of the saliency detection.

Image saliency detection is very useful in many computer vision tasks while it still remains a challenging problem. In this paper, we propose a new computational saliency detection model which is implemented with a coarse to fine strategy under the Bayesian framework. First, saliency points are applied to get a coarse location of the saliency region. And then, based on the rough region, we compute a prior map for the Bayesian model to achieve the final saliency map. Experimental results on a public available dataset show the effectiveness of the proposed prior map and the strength of our saliency map compared with several previous method.

Visual saliency detection plays a significant role in the fields of computer vision. In this paper, we introduce a novel saliency detection method based on weighted linear multiple kernel learning (WLMKL) framework, which is able to adaptively combine different contrast measurements in a supervised manner. As most influential factor is contrast operation in bottom-up visual saliency, an average weighted corner-surround contrast (AWCSC) is first designed to measure local visual saliency. Combined with common-used center-surrounding contrast (CESC) and global contrast (GC), three types of contrast operations are fed into our WLMKL framework to produce the final saliency map. We show that the assigned weights for each contrast feature maps are always normalized in our WLMKL formulation. In addition, the proposed approach benefits from the advantages of the contribution of each individual contrast feature maps, yielding more robust and accurate saliency maps. We evaluated our method for two main visual saliency detection tasks: human fixed eye prediction and salient object detection. The extensive experimental results show the effectiveness of the proposed model, and demonstrate the integration is superior than individual subcomponent.

This article proposes a bottom-up visual saliency model that uses the wavelet transform to conduct multiscale analysis and computation in the frequency domain. First, we compute the multiscale magnitude spectra by performing a wavelet transform to decompose the magnitude spectrum of the discrete cosine coefficients of an input image. Next, we obtain multiple saliency maps of different spatial scales through an inverse transformation from the frequency domain to the spatial domain, which utilizes the discrete cosine magnitude spectra after multiscale wavelet decomposition. Then, we employ an evaluation function to automatically select the two best multiscale saliency maps. A final saliency map is generated via an adaptive integration of the two selected multiscale saliency maps. The proposed model is fast, efficient, and can simultaneously detect salient regions or objects of different sizes. It outperforms state-of-the-art bottom-up saliency approaches in the experiments of psychophysical consistency, eye fixation prediction, and saliency detection for natural images. In addition, the proposed model is applied to automatic ship detection in optical satellite images. Ship detection tests on satellite data of visual optical spectrum not only demonstrate our saliency model's effectiveness in detecting small and large salient targets but also verify its robustness against various sea background disturbances.

A novel visual saliency detection method for infrared video sequences

Saliency detection is a technique for automatically extracting regions of interest from the background and has been widely used in the computer vision field. This study proposes a simple and effective saliency detection method combining color contrast and hash fingerprint. In our solution, the input image is segmented into nonoverlapping superpixels, so as to perform the saliency detection at the region level to reduce computational complexity. A background optimization selection is used to construct an accurate background template. Based on this, a saliency map that highlights the whole salient region is obtained by estimating color contrast. Besides, another saliency map that enhances the salient region while restraining the background is also generated through hash fingerprint matching. Ultimately, the final saliency map can be obtained by fusing the two saliency maps. Comparing the performance with other methods, the proposed algorithm works better even in the presence of complex background or very large salient regions.

Stereoscopic perception is an important part of human visual system that allows the brain to perceive depth. However, depth information has not been well explored in existing saliency detection models. In this letter, a novel saliency detection method for stereoscopic images is proposed. Firstly, we propose a measure to evaluate the reliability of depth map, and use it to reduce the influence of poor depth map on saliency detection. Then, the input image is represented as a graph, and the depth information is introduced into graph construction. After that, a new definition of compactness using color and depth cues is put forward to compute the compactness saliency map. In order to compensate the detection errors of compactness saliency when the salient regions have similar appearances with background, foreground saliency map is calculated based on depth-refined foreground seeds selection mechanism and multiple cues contrast. Finally, these two saliency maps are integrated into a final saliency map through weighted-sum method according to their importance. Experiments on two publicly available stereo datasets demonstrate that the proposed method performs better than other 10 state-of-the-art approaches.

Since 20th century generation, image segmentation has been attached great importance by the people and thousands of the segmentation algorithms have been proposed. In practice, in order to facilitate research and analysis of the images that often only needs to be interested in certain parts of the image part is divided into a certain properties of a particular area, in order to use the target further, we also need to complete for their separation. This paper integrates the visual attention model to propose the novel image segmentation algorithm based on visual saliency detection and the integrated feature extraction. We have achieved the better numerical performance compared with other state-of-the-art methodologies. The propose method integrates the local and the regional features to construct the global optimized saliency map to serve as the basis for segmentation. The entropy of the image is used for determining the threshold and the effectiveness is proved through experiment.

Saliency detection is prerequisite for many computer vision tasks. The existing frequency domain models can not always detect a complete object. We propose a novel salient object detection model based on an optimized amplitude spectrum. This model computes saliency map in two steps. Firstly, we optimize amplitude spectrum by smoothing the peaks in log amplitude spectrum. The raw saliency maps are computed by combining the optimized amplitude spectrum and the original phase spectrum according to different thresholds. Secondly, we compute the entropy of raw saliency maps and select the raw saliency map with the smallest value of entropy as the final saliency map. Our model detects more complete object region. By testing on the databases ASD, MSRA10K, DUT-OMRON and SED2, experiments demonstrate that the proposed model outperforms the state-of-the-art models.

Visual saliency detection is usually a prerequisite for image processing tasks like object segmentation, object recognition and information compression. In this work, we first present a novel method that combines cognitive-based objectness and image-based saliency to obtain a better saliency map with less negligible background information. Then, by introducing some top-down attention priors, we further propose a computational selective attention model for object segmentation task on the saliency map. Experiments have shown that our method obviously refines the saliency map of several state-of-the-art algorithms, and our selective attention model meanwhile evidently improves salient object segmentation performance in the challenging saliency benchmark-Extend Complex Scene Saliency Dataset (ECSSD).

Visual saliency detection has become a challenging area in computer vision. In this paper, we propose a novel region based saliency detection model which considers background priors. The proposed method consists of two successive steps - region weighting and contrast computing. In the step of region weighting, we calculate the region weight for each region by region-level image feature and a log-linear prediction model. In the step of contrast computing, we propose a modified contrast computing algorithm by exploiting the advantage of region weights for bottom-up saliency detection. The experimental results on two datasets prove that our method effectively improves the performance on visual saliency detection.

This study aims to improve the quality of multi-view video, and designs a new method by integrating Convolutional neural network (CNN), visual saliency detection and image enhancement theory. The experimental results show that the proposed visual saliency detection model and convolution filter sensor have made remarkable progress. The superiority of the visual saliency detection model is helpful to accurately locate the key features of the image and provide accurate targets for subsequent enhancement processing. The convolution filter sensor improves the peak value of the image, narrows the gap with the original image and improves the visual effect. Supplementary experiments further verify the effectiveness of the method. Through the quantitative comparison between SSIM and MS-SSIM, the method is obviously superior to the existing methods on several data sets, showing a robust video quality enhancement effect. These results highlight the superiority and robustness of the method, and bring strong empirical support to the field of multi-view video quality enhancement, which is expected to have an important impact in practical applications.

Salient object detection in a real-time environment demands high accuracy with less computation time. It is a changeling task to investigate a saliency model which improves saliency detection accuracy as well as reduces computation time. In this paper, we propose a hybrid model that improves detection accuracy with low computational time. The input image is simplified and clustered at multiple scales using SLIC and K-means clustering algorithms respectively. Gaussian Mixture Model (GMM) is developed on various color components of the digital image at multiple scales. The parameters of GMM are learnt using Expectation Maximization (EM) algorithm. The spatial variance of each color component is determined using GMM parameters and hence object position is estimated. Further, spatial variance of color components and object position is exploited to compute saliency map at a scale level. Afterwards, all the saliency maps generated across various scales are linearly combined to produce the final saliency map. The performance of the proposed model is compared in terms of Precision, Recall, F-Measure, Area under the Curve (AUC), Receiver Operating Characteristics (ROC) and Mean Absolute Error (MAE). Extensive experiments on six publicly available datasets viz. MSRA10K, DUT-OMRON, ECSSD, PASCAL-S, SED2, and THUR15K show that the proposed model outperforms or comparable against 11 state-of-the-art methods of the last decade. The key features of the proposed method are object completeness and efficiency in terms of computational time.

Though there are many computational models proposed for saliency detection, few of them take object boundary information into account. This paper presents a hierarchical saliency detection model incorporating probabilistic object boundaries, which is based on the observation that salient objects are generally surrounded by explicit boundaries and show contrast with their surroundings. We perform adaptive thresholding operation on ultrametric contour map, which leads to hierarchical image segmentations, and compute the saliency map for each layer based on the proposed robust center bias, border bias, color dissimilarity and spatial coherence measures. After a linear weighted combination of multi-layer saliency maps, and Bayesian enhancement procedure, the final saliency map is obtained. Extensive experimental results on three challenging benchmark datasets demonstrate that the proposed model outperforms eight state-of-the-art saliency detection models.

A salient region is the most distinctive part of the image that captures human's attention. Saliency detection is a fundamental characteristic of the human visual system. Finding computational models which are able to detect salient regions is a challenging task for image processing and computer vision applications. Salient regions of various sizes can be detected from different scales. Therefore, selecting the best scales is an important issue. In this paper, an efficient multi-scale method to find salient regions is proposed. In order to include more features in evaluating saliency of a pixel, feature maps are generated using components of both the RGB and YUV color spaces. These features are combined into quaternions. Detecting salient regions of different sizes is addressed by utilizing a scale space analysis. Salient regions are detected by convolving the image amplitude spectrum with a low-pass Gaussian kernel of multiple scales. To incorporate more meaningful information, more than one scale is considered based on entropy criterion. The final saliency map is generated by normalizing the weighted saliency maps of these scales. Experiments are conducted on a dataset of natural images to evaluate the performance of the proposed method. Results show that the proposed method provides larger values of area under receiver operating characteristics curve, precision, recall and F-measure, in comparison to some of the state-of-the-art methods.