A Model for Saliency Detection Using NMFsc Algorithm

Biologically inspired visual object tracking has been very popular in the last decades. Several methods have exploited some visual cognitive mechanisms to outperform trackers purely based on machine vision in accuracy and speed. Although they have shown outstanding performance on recent benchmarks, they are still far from achieving the performance of a human in tracking. Visual attention is an important mechanism, which can be applied by saliency detection models. Saliency detection models can be determined in the spatial domain or the frequency domain. Spatial domain based models mostly have complex computations and are time-consuming. Thus, we have improved and recruited Frequency Divisive Normalization (FDN) model as a saliency detection method to propose a bio-inspired object tracking algorithm. This algorithm has been developed based on Fast Fourier Transform (FFT) and uses spectral features in order to achieve a high-speed tracker. It also can be launched on normal hardware. FDN model is based on Feature Integration Theory (FIT) and operates all the processes of saliency detection in the frequency domain, including decomposing the image to the features, lateral inhibition interaction, combining all features, and computing the saliency map. In addition to, extracting color, intensity and orientation it uses spatial frequency features, which is rarely used by other trackers. Extensive evaluations on large-scale benchmark datasets show that the proposed method has good performance with respect to its runtime.

Visual saliency detection based on homology similarity and an experimental evaluation

The past decade has witnessed the popularity of video conferencing, such as FaceTime and Skype. In video conferencing, almost every frame has a human face. Hence, it is necessary to predict attention on face videos by saliency detection, as saliency can be used as a guidance of regionof- interest (ROI) for the content-based applications. To this end, this paper proposes a novel approach for saliency detection in single-face videos. From the data-driven perspective, we first establish an eye tracking database which contains fixations of 70 single-face videos viewed by 40 subjects. Through analysis on our database, we investigate that most attention is attracted by face in videos, and that attention distribution within a face varies with regard to face size and mouth movement. Inspired by the previous work which applies Gaussian mixture model (GMM) for face saliency detection in still images, we propose to model visual attention on face region for videos by dynamic GMM (DGMM), the variation of which relies on face size, mouth movement and facial landmarks. Then, we develop a long shortterm memory (LSTM) neural network in estimating DGMM for saliency detection of single-face videos, so called LSTM-DGMM. Finally, the experimental results show that our approach outperforms other state-of-the-art approaches in saliency detection of single-face videos.

In existing superpixel-wise saliency detection algorithms, superpixel generation often is an isolated preprocessing step. The performance of saliency maps is determined by the accuracy of superpixels to a certain extent. However, it is still a challenge to develop a stable superpixel generation method. In this article, we attempt to incorporate the superpixel generation and saliency calculation steps into an end-to-end trainable deep network. First, we employ a recently proposed differentiable superpixel generation method to over-segment the synthetic aperture radar (SAR) images, which outputs the possibility that the pixels assigned to neighbor superpixels (soft superpixel). In saliency calculation part, as one of our main contributions, we propose a differentiable and computationally simple saliency model, i.e., lacunarity cue. It is inspired by the fact that generally the backscattering intensity of regions of interest (ROIs) in SAR images irregularly fluctuates, while the areas with consistent pixels are often ignored as the clusters. We improve the pixelwise box differential dimension algorithm to measure the irregularity of scattering points in a superpixel. The superpixel generation and saliency calculation can be implemented under a unified deep network. Hence, the shapes of the superpixels can be iteratively adjusted according to the saliency maps until the ROIs are correctly detected. Experiments on real SAR images with different sizes and scenes show that the saliency maps can effectively highlight the target areas, thus outperforming the state-of-the-art saliency detection models.

The effectiveness of depth information in saliency detection has been fully proved. However, it is still worth exploring how to utilize the depth information more efficiently. Erroneous depth information may cause detection failure, while non-salient objects may be closer to the camera which also leads to erroneously emphasis on non-salient regions. Moreover, most of the existing RGB-D saliency detection models have poor robustness when the salient object touches the image boundaries. To mitigate these problems, we propose a multi-stage saliency detection model with the bilateral absorbing Markov chain guided by depth information. The proposed model progressively extracts the saliency cues with three level (low-, mid-, and high-level) stages. First, we generate low-level saliency cues by explicitly combining color and depth information. Then, we design a bilateral absorbing Markov chain to calculate mid-level saliency maps. In mid-level, to suppress boundary touch problem, we present the background seed screening mechanism (BSSM) for improving the construction of the two-layer sparse graph and better selecting background-based absorbing nodes. Furthermore, the cross-modal multi-graph learning model (CMLM) is designed to fully explore the intrinsic complementary relationship between color and depth information. Finally, to obtain a more highlighted and homogeneous saliency map in high-level, we structure a depth-guided optimization module which combines cellular automata and suppression-enhancement function pair. This optimization module refines the saliency map in color space and depth space, respectively. Comprehensive experiments on three challenging benchmark datasets demonstrate the effectiveness of our proposed method both qualitatively and quantitatively.

Data-driven saliency detection has attracted strong interest as a result of applying convolutional neural networks to the detection of eye fixations. Although a number of image-based salient object and fixation detection models have been proposed, video fixation detection still requires more exploration. Different from image analysis, motion and temporal information is a crucial factor affecting human attention when viewing video sequences. Although existing models based on local contrast and low-level features have been extensively researched, they failed to simultaneously consider interframe motion and temporal information across neighboring video frames, leading to unsatisfactory performance when handling complex scenes. To this end, we propose a novel and efficient video eye fixation detection model to improve the saliency detection performance. By simulating the memory mechanism and visual attention mechanism of human beings when watching a video, we propose a step-gained fully convolutional network by combining the memory information on the time axis with the motion information on the space axis while storing the saliency information of the current frame. The model is obtained through hierarchical training, which ensures the accuracy of the detection. Extensive experiments in comparison with 11 state-of-the-art methods are carried out, and the results show that our proposed model outperforms all 11 methods across a number of publicly available datasets.

In this paper, a novel and efficient framework by exploiting Quaternionic Distance Based Weber Local Descriptor (QDWLD) and object cues is proposed for image saliency detection. In contrast to the existing saliency detection models, the advantage of the proposed approach is that it can combine quaternion number system and object cues simultaneously, which is independent of image contents and scenes. Firstly, QDWLD, which was initially designed for detecting outliers in color images, is used to represent the directional cues in an image. Meanwhile, two low-level priors, namely the Convex-Hull-Based center and color contrast cue of the image, are utilized and fused as an object-level cue. Finally, by combining QDWLD with object cues, a reliable saliency map of the image can be computed and estimated. Experimental results, based on two widely used and openly available database, show that the proposed method is able to produce reliable and promising salient maps/estimations, compared to other state-of-the-art saliency-detection models.

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.

Salient regions usually carry important information in images. Existing feature encryption algorithms aim at extracting edge features as significant information rather than salient regions for encryption purpose. Moreover, most of them protect significant information by transforming the input image into texture-like or noise-like encrypted image which is obviously a visual sign of encrypted image, and thus can be easily attacked. In this paper, we propose a salient regions encryption scheme to generate visually meaningful ciphertext. First, salient regions are efficiently extracted by a saliency detection model in the compressed domain. Then we pre-encrypt these salient regions by a chaos-based encryption algorithm. With optical encryption theory, the pre-encrypted salient regions are finally transformed into a visually meaningful ciphertext. To the best of our knowledge, it is the first time to use salient regions as important visual information for encryption to obtain cipertext in images. The experimental results demonstrate that the salient regions can be largely hidden with the proposed method.

Saliency detection is an effective tool to extract regions of interest (ROIs) from remote sensing images. However, some existing saliency detection models focus on extracting ROI from a single image, which cannot accurately detect ROI against complex background interference. In this paper, a novel visual saliency analysis and ROI extraction model is proposed to effectively extract common ROIs from remote sensing images and exclude images without ROIs. Firstly, the single saliency maps are generated by frequency-tuned (FT) method. Secondly, the cluster method based on synthesized features is proposed to group regions with similar feature into a cluster for multiple images. Thirdly, computing the mean of saliency value as the cluster saliency suppresses the saliency value of non-common ROIs. Finally, a ROI extraction method based on the maximum saliency value is proposed to extract ROIs while eliminating the image without ROIs. Experimental results indicate our model outperforms other state-of-the-art saliency detection models, achieving highest ROC and maximal PRF values.

Image saliency detection provides a powerful tool for predicting where human tends to look at in an image, which has been a long attempt for the computer vision community. In this paper, we propose a biologically-inspired model for computing image saliency. At first, a set of basis functions that accords with visual responses to natural stimuli is learned by using eye-fixation patches from an eye-tracking dataset. Three features are then derived based on the learned basis functions including continuity, clutter contrast, and local contrast. Finally, these three features are combined into the saliency map. The proposed approach is easy to implement and can be used in many image and video content analysis applications. Experiments on a large-scale benchmark dataset and comparisons with a number of the state-of-the-art approaches demonstrate its superiority.

This work presents a method for salt dome detection in seismic images based on a Context-Aware Saliency (CAS) detection model. Seismic data can easily add up to hundred of gigabytes and terabytes in size. However, the key features or structural information that are of interest to the seismic interpreters are quite few. These features include salt domes, fault and other geological features that have the potential of indicating the presence of oil reservoir. A new method for extracting the most perceptual relevant features in seismic images based on the CAS model is proposed. The efficiency of this method in detecting the most salient structures in a seismic image such as salt dome is demonstrated through a series of experiment on real data set with various spatial contents.

Detecting and segmenting objects within whole slide images is essential in computational pathology workflow. Self-supervised learning (SSL) is appealing to such annotation-heavy tasks. Despite the extensive benchmarks in natural images for dense tasks, such studies are, unfortunately, absent in current works for pathology. Our paper intends to narrow this gap. We first benchmark representative SSL methods for dense prediction tasks in pathology images. Then, we propose concept contrastive learning (ConCL), an SSL framework for dense pre-training. We explore how ConCL performs with concepts provided by different sources and end up with proposing a simple dependency-free concept generating method that does not rely on external segmentation algorithms or saliency detection models. Extensive experiments demonstrate the superiority of ConCL over previous state-of-the-art SSL methods across different settings. Along our exploration, we distill several important and intriguing components contributing to the success of dense pre-training for pathology images. We hope this work could provide useful data points and encourage the community to conduct ConCL pre-training for problems of interest. Code is available at https://github.com/TencentAILabHealthcare/ConCL.KeywordsPathology image analysisWhole slide imageSelf-supervised learningObject detectionInstance segmentationPre-training

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.

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.