Spatiotemporal Saliency Model Research Articles

Spatio-temporal Saliency-based Motion Vector Refinement for Frame Rate Up-conversion

A spatio-temporal saliency-based frame rate up-conversion (FRUC) approach is proposed, which achieves better quality of interpolated frames and invalidates existing texture variation-based FRUC detectors. A spatio-temporal saliency model is designed to select salient frames. After obtaining initial motion vector field by texture- and color-based bilateral motion estimation, two motion vector refining (MVR) schemes are adopted for high and low saliency frames to hierarchically refine the motion vectors, respectively. To produce high-quality interpolated frames, image enhancement are performed for salient frames after frame interpolation. Due to distinct MVR schemes, there are different degrees of texture information in interpolated frames. Some edge and texture information is supplemented into salient frames as post-processing, which can invalidate existing texture variation-based FRUC detectors. Experimental results show that the proposed approach outperforms state-of-the-art works in both objective and subjective qualities of interpolated frames, and achieves the purpose of FRUC anti-forensics.

A behaviorally inspired fusion approach for computational audiovisual saliency modeling

Human attention is highly influenced by multi-modal combinations of perceived sensory information and especially audiovisual information. Although systematic behavioral experiments have provided evidence that human attention is multi-modal, most bottom-up computational attention models, namely saliency models for fixation prediction, focus on visual information, largely ignoring auditory input. In this work, we aim to bridge the gap between findings from neuroscience concerning audiovisual attention and the computational attention modeling, by creating a 2-D bottom-up audiovisual saliency model. We experiment with various fusion schemes for integrating state-of-the-art auditory and visual saliency models in a single audiovisual attention/saliency model based on behavioral findings, that we validate in two experimental levels: (1) using results from behavioral experiments aiming to reproduce the results in a mostly qualitative manner and to ensure that our modeling is in line with behavioral findings, and (2) using 6 different databases with audiovisual human eye-tracking data. For this last purpose, we have also collected eye-tracking data for two databases: ETMD, a movie database that contains highly edited videos (movie clips), and SumMe, a database that contains unstructured and unedited user videos. Experimental results indicate that our proposed audiovisual fusion schemes in most cases improve performance compared to visual-only models, without any prior knowledge of the video/audio content. Also, they can be generalized and applied to any auditory saliency model and any visual spatio-temporal saliency model.

Quality-Oriented Perceptual HEVC Based on the Spatiotemporal Saliency Detection Model.

Perceptual video coding (PVC) can provide a lower bitrate with the same visual quality compared with traditional H.265/high efficiency video coding (HEVC). In this work, a novel H.265/HEVC-compliant PVC framework is proposed based on the video saliency model. Firstly, both an effective and efficient spatiotemporal saliency model is used to generate a video saliency map. Secondly, a perceptual coding scheme is developed based on the saliency map. A saliency-based quantization control algorithm is proposed to reduce the bitrate. Finally, the simulation results demonstrate that the proposed perceptual coding scheme shows its superiority in objective and subjective tests, achieving up to a 9.46% bitrate reduction with negligible subjective and objective quality loss. The advantage of the proposed method is the high quality adapted for a high-definition video application.

Deep3DSaliency: Deep Stereoscopic Video Saliency Detection Model by 3D Convolutional Networks.

Stereoscopic saliency detection plays an important role in various stereoscopic video processing applications. However, conventional stereoscopic video saliency detection methods mainly use independent low-level features instead of extracting them automatically, and thus, they ignore the intrinsic relationship between the spatial and temporal information. In this paper, we propose a novel stereoscopic video saliency detection method based on 3D convolutional neural networks, namely Deep 3D Video Saliency (Deep3DSaliency). The proposed network consists of two sub-models: Spatiotemporal Saliency Model (STSM), and Stereoscopic Saliency Aware Model (SSAM). STSM directly takes three consecutive video frames as the input to extract visual spatiotemporal features, while SSAM attempts to further infer the depth and semantic features from the left and right video frames by shared parameters from STSM. The visual spatiotemporal features from STSM, and the depth and semantic features from SSAM are learned by an alternating optimization scheme. Finally, all these saliency-related features are combined together for the final stereoscopic saliency detection via 3D deconvolution. Experimental results show the superior performance of the proposed model over other existing ones in saliency estimation for 3D video sequences.

Video saliency detection via bagging-based prediction and spatiotemporal propagation

The task of spatiotemporal saliency detection is to distinguish the salient objects from background across all the frames in the video. Although many spatiotemporal models have been designed from various aspects, it is still a very challenging task for handing the unconstrained videos with complicated motions and complex scenes. Therefore, in this paper we propose a novel spatiotemporal saliency model to estimate salient objects in unconstrained videos. Specifically, a bagging-based saliency prediction model, i.e. an ensembling regressor, which is the combination of random forest regressors learned from undersampled training sets, is first used to perform saliency prediction for each current frame. Then, both forward and backward propagation within a local temporal window are deployed on each current frame to make a complement to the predicted saliency map and yield the temporal saliency map, in which the backward propagation is constructed based on the temporary saliency estimation of the following frames. Finally, by building the appearance and motion based graphs in a parallel way, spatial propagation is employed over the temporal saliency map to generate the final spatiotemporal saliency map. Through experiments on two challenging datasets, the proposed model consistently outperforms the state-of-the-art models for popping out salient objects in unconstrained videos.

Saliency Detection for Unconstrained Videos Using Superpixel-Level Graph and Spatiotemporal Propagation

This paper proposes an effective spatiotemporal saliency model for unconstrained videos with complicated motion and complex scenes. First, superpixel-level motion and color histograms as well as global motion histogram are extracted as the features for saliency measurement. Then a superpixel-level graph with the addition of a virtual background node representing the global motion is constructed, and an iterative motion saliency (MS) measurement method that utilizes the shortest path algorithm on the graph is exploited to reasonably generate MS maps. Temporal propagation of saliency in both forward and backward directions is performed using efficient operations on inter-frame similarity matrices to obtain the integrated temporal saliency maps with the better coherence. Finally, spatial propagation of saliency both locally and globally is performed via the use of intra-frame similarity matrices to obtain the spatiotemporal saliency maps with the even better quality. The experimental results on two video data sets with various unconstrained videos demonstrate that the proposed model consistently outperforms the state-of-the-art spatiotemporal saliency models on saliency detection performance.

A novel visual saliency detection method for infrared video sequences

Infrared video applications such as target detection and recognition, moving target tracking, and so forth can benefit a lot from visual saliency detection, which is essentially a method to automatically localize the “important” content in videos. In this paper, a novel visual saliency detection method for infrared video sequences is proposed. Specifically, for infrared video saliency detection, both the spatial saliency and temporal saliency are considered. For spatial saliency, we adopt a mutual consistency-guided spatial cues combination-based method to capture the regions with obvious luminance contrast and contour features. For temporal saliency, a multi-frame symmetric difference approach is proposed to discriminate salient moving regions of interest from background motions. Then, the spatial saliency and temporal saliency are combined to compute the spatiotemporal saliency using an adaptive fusion strategy. Besides, to highlight the spatiotemporal salient regions uniformly, a multi-scale fusion approach is embedded into the spatiotemporal saliency model. Finally, a Gestalt theory-inspired optimization algorithm is designed to further improve the reliability of the final saliency map. Experimental results demonstrate that our method outperforms many state-of-the-art saliency detection approaches for infrared videos under various backgrounds.

Fusing disparate object signatures for salient object detection in video

We present a novel spatiotemporal saliency model for object detection in videos. In contrast to previous methods focusing on exploiting or incorporating different saliency cues, the proposed method aims to use object signatures which can be identified by any kinds of object segmentation methods. We integrate two distinctive saliency maps, which are respectively computed from object proposals of an appearance-dominated method and a motion-dominated algorithm, to obtain a refined spatiotemporal saliency maps. This enables the method to achieve good robustness and precision in identifying salient objects in videos under various challenging conditions. First, an improved appearance-based and a modified motion-based segmentation approaches are separately utilized to extract two kinds of candidate foreground objects. Second, with these captured object signatures, we design a new approach to filter the extracted noisy object pixels and label foreground superpixels in each object signature channel. Third, we introduce a foreground connectivity saliency measure to compute two types of saliency maps, from which an adaptive fusion strategy is exploited to obtain the final spatiotemporal saliency maps for salient object detection in a video. Both quantitative and qualitative experiments on several challenging video benchmarks demonstrate that the proposed method outperforms existing state-of-the-art approaches.

Robust segmentation of moving objects in video based on spatiotemporal visual saliency and active contour model

This paper presents an algorithm for automatic segmentation of moving objects in video based on spatiotemporal visual saliency and an active contour model. Our algorithm exploits the visual saliency and motion information to build a spatiotemporal visual saliency map used to extract a moving region of interest. This region is used to automatically provide the seeds for the convex active contour (CAC) model to segment the moving object accurately. The experiments show a good performance of our algorithm for moving object segmentation in video without user interaction, especially on the SegTrack dataset.

A novel spatio-temporal saliency approach for robust dim moving target detection from airborne infrared image sequences

Dim moving target detection from infrared image sequences, which lags behind the visual perception ability of humans, has attracted considerable interest from researchers due to its crucial role in airborne surveillance systems. This paper proposes a novel spatio-temporal saliency model to cope with the infrared dim moving target detection problem. Based on a closed-form solution derived from regularized feature reconstruction, a local adaptive contrast operation is proposed, whereby the spatial saliency map and the temporal saliency map can be calculated on the spatial domain and the temporal domain. In order to depict the motion consistency characteristic of the moving target, this paper also proposes a transmission operation to generate the trajectory prediction map. The fused result of the spatial saliency map, the temporal saliency map, and the trajectory prediction map is called the “spatio-temporal saliency map” in this paper, from which the target of interest can be easily segmented. A diverse test dataset comprised of three infrared image sequences under different backgrounds was collected to evaluate the proposed model; and extensive experiments confirmed that the proposed spatio-temporal saliency model can achieve much better detection performance than the state-of-the-art approaches.

A perceptually based spatio-temporal computational framework for visual saliency estimation

The purpose of this paper is to demonstrate a perceptually based spatio-temporal computational framework for visual saliency estimation. We have developed a new spatio-temporal visual frontend based on biologically inspired 3D Gabor filters, which is applied on both the luminance and the color streams and produces spatio-temporal energy maps. These volumes are fused for computing a single saliency map and can detect spatio-temporal phenomena that static saliency models cannot find. We also provide a new movie database with eye-tracking annotation. We have evaluated our spatio-temporal saliency model on the widely used CRCNS-ORIG database as well as our new database using different fusion schemes and feature sets. The proposed spatio-temporal computational framework incorporates many ideas based on psychological evidences and yields significant improvements on spatio-temporal saliency estimation.

Spatiotemporal saliency detection based on superpixel-level trajectory

In this paper, we propose a novel spatiotemporal saliency model based on superpixel-level trajectories for saliency detection in videos. The input video is first decomposed into a set of temporally consistent superpixels, on which superpixel-level trajectories are directly generated, and motion histograms at superpixel level as well as frame level are extracted. Based on motion vector fields of multiple successive frames, the inside–outside maps are estimated to roughly indicate whether pixels are inside or outside objects with motion different from background. Then two descriptors, i.e. accumulated motion histogram and trajectory velocity entropy, are exploited to characterize the short-term and long-term temporal features of superpixel-level trajectories. Based on trajectory descriptors and inside–outside maps, superpixel-level trajectory distinctiveness is evaluated and trajectory classification is performed to obtain trajectory-level temporal saliency. Superpixel-level and pixel-level temporal saliency maps are generated in turn by exploiting motion similarity with neighboring superpixels around each trajectory, and color-spatial similarity with neighboring superpixels around each pixel, respectively. Finally, a quality-guided fusion method is proposed to integrate the pixel-level temporal saliency map with the pixel-level spatial saliency map, which is generated based on global contrast and spatial sparsity of superpixels, to generate the pixel-level spatiotemporal saliency map with reasonable quality. Experimental results on two public video datasets demonstrate that the proposed model outperforms the state-of-the-art spatiotemporal saliency models on saliency detection performance.

Kernel regression in mixed feature spaces for spatio-temporal saliency detection

Spatio-temporal saliency detection has attracted lots of research interests due to its competitive performance on wide multimedia applications. For spatio-temporal saliency detection, existing bottom-up algorithms often over-simplify the fusion strategy, which results in the inferior performance than the human vision system. In this paper, a novel bottom-up spatio-temporal saliency model is proposed to improve the accuracy of attentional region estimation in videos through fully exploiting the merit of fusion. In order to represent the space constructed by several types of features such as location, appearance and temporal cues extracted from video, kernel regression in mixed feature spaces (KR-MFS) including three approximation entity-models is proposed. Using KR-MFS, a hybrid fusion strategy which considers the combination of spatial and temporal saliency of each individual unit and incorporates the impacts from the neighboring units is presented and embedded into the spatio-temporal saliency model. The proposed model has been evaluated on the publicly available dataset. Experimental results show that the proposed spatio-temporal saliency model can achieve better performance than the state-of-the-art approaches.

Spatiotemporal saliency model for small moving object detection in infrared videos

In this paper, a novel spatiotemporal saliency model based on three-dimensional Difference-of-Gaussians filters is proposed for small moving object detection in infrared videos. First, instead of utilizing the spatial Difference-of-Gaussians (DoG) filter which has been used to build saliency model for static images, we propose to extend the spatial DoG filter to construct three-dimensional (3D) Difference-of-Gaussians filters for measuring the center-surround difference in the spatiotemporal receptive field. Second, an effective spatiotemporal saliency model is generated based on these filters. This model provides a good basis for accurate and robust infrared small moving object detection. Experimental results show that the proposed saliency model consistently outperforms state-of-the-art saliency models for infrared moving object detection under various complex backgrounds.

Superpixel-Based Spatiotemporal Saliency Detection

This paper proposes a superpixel-based spatiotemporal saliency model for saliency detection in videos. Based on the superpixel representation of video frames, motion histograms and color histograms are extracted at the superpixel level as local features and frame level as global features. Then, superpixel-level temporal saliency is measured by integrating motion distinctiveness of superpixels with a scheme of temporal saliency prediction and adjustment, and superpixel-level spatial saliency is measured by evaluating global contrast and spatial sparsity of superpixels. Finally, a pixel-level saliency derivation method is used to generate pixel-level temporal and spatial saliency maps, and an adaptive fusion method is exploited to integrate them into the spatiotemporal saliency map. Experimental results on two public datasets demonstrate that the proposed model outperforms six state-of-the-art spatiotemporal saliency models in terms of both saliency detection and human fixation prediction.

Video Segmentation of Multiresolution Dynamic Spatiotemporal Model Based on Quaternion Wavelet Transform

How to achieve a meaningful video representation is an important problem in various research communities. Automatically segmenting non-specific objects is an open problem. To deal with the error segmentation problem of the existing video algorithms under dynamic scenes, we propose a dynamic spatiotemporal saliency model based on the quaternion wavelet transform for video segmentation in this paper, which has the capability of segmenting the salient objects from moving background automatically. The model is a dynamic combination of the temporal attention model and static salient model. In temporal attention model, motion contrast information can be computed from the phase disparity between two consecutive frames. The phase is extracted from quaternionic pyramid. In static salient model, the spatial attention information is computed by an inverse quaternion wavelet transform over the set of scale-weighted center-surround responses. The scale-weighting function has been optimized to better replicate psychophysical data on color appearance. We combine the two kinds of attention information to get the preliminary results and use Grabuct algorithm for the optimization of the result finally. The segmentation and comparison experimental results demonstrate the validity of proposed algorithm.

A Spatiotemporal Saliency Model for Video Surveillance

A video sequence is more than a sequence of still images. It contains a strong spatial–temporal correlation between the regions of consecutive frames. The most important characteristic of videos is the perceived motion foreground objects across the frames. The motion of foreground objects dramatically changes the importance of the objects in a scene and leads to a different saliency map of the frame representing the scene. This makes the saliency analysis of videos much more complicated than that of still images. In this paper, we investigate saliency in video sequences and propose a novel spatiotemporal saliency model devoted for video surveillance applications. Compared to classical saliency models based on still images, such as Itti’s model, and space–time saliency models, the proposed model is more correlated to visual saliency perception of surveillance videos. Both bottom-up and top-down attention mechanisms are involved in this model. Stationary saliency and motion saliency are, respectively, analyzed. First, a new method for background subtraction and foreground extraction is developed based on content analysis of the scene in the domain of video surveillance. Then, a stationary saliency model is setup based on multiple features computed from the foreground. Every feature is analyzed with a multi-scale Gaussian pyramid, and all the features conspicuity maps are combined using different weights. The stationary model integrates faces as a supplement feature to other low level features such as color, intensity and orientation. Second, a motion saliency map is calculated using the statistics of the motion vectors field. Third, both motion saliency map and stationary saliency map are merged based on center-surround framework defined by an approximated Gaussian function. The video saliency maps computed from our model have been compared to the gaze maps obtained from subjective experiments with SMI eye tracker for surveillance video sequences. The results show strong correlation between the output of the proposed spatiotemporal saliency model and the experimental gaze maps.

Modelling Spatio-Temporal Saliency to Predict Gaze Direction for Short Videos

This paper presents a spatio-temporal saliency model that predicts eye movement during video free viewing. This model is inspired by the biology of the first steps of the human visual system. The model extracts two signals from video stream corresponding to the two main outputs of the retina: parvocellular and magnocellular. Then, both signals are split into elementary feature maps by cortical-like filters. These feature maps are used to form two saliency maps: a static and a dynamic one. These maps are then fused into a spatio-temporal saliency map. The model is evaluated by comparing the salient areas of each frame predicted by the spatio-temporal saliency map to the eye positions of different subjects during a free video viewing experiment with a large database (17000 frames). In parallel, the static and the dynamic pathways are analyzed to understand what is more or less salient and for what type of videos our model is a good or a poor predictor of eye movement.