Saliency detection for surface defects of ceramic tile

The ViBe algorithm is a powerful technique for the background detection and subtraction in video sequences. Compared to the state-of-the-art algorithms, ViBe algorithm is better in fast speed and less memory consumption. However, when applying ViBe algorithm to the moving objects appeared in the first frame of videos, all pixels in first frame will be used to build the background model that will result in the foreground pixels in sample set. This problem causes the ghost areas emerge. And it will remain for a long time. In this paper, a salient region detection based ViBe algorithm is proposed to eliminate the ghost areas fast. First, the foreground region is extracted from the first frame of videos using the salient region detection algorithm. According to the result of salient region detection, the background area of image is separated from the foreground area. The foreground pixels are dislodged from the sample set. Then, only background pixels are used for background model initialization. The experimental result shows that the improved algorithm can eliminate ghost in few frames quickly.

Frequency-tuned saliency detection analyzes image saliency from the perspective of frequency domain and fully combines image segmentation method, which outputs well- defined boundaries of salient objects. However, the method ignores spatial relationships across image parts. This paper proposes an improved saliency detection method on the basis of the frequency-tuned method. In this method, we first segment the input image into regions and then analyze the image from the frequency domain. After that, we preprocess it using Gaussian filter to eliminate noise and coding artifacts. For each region, we can get saliency map in region-level based on region contrast. Finally, salient regions are selected by winner-take-all (WTA) neural network and Inhibition of Return(IOR) mechanism. The proposed salient region detection algorithm combines the virtues of frequency-tuning and region contrast. The experimental results show the feasibility and validity of this algorithm. Keywords-Image retrieval; Semantic gap; Saliency detection; Region-level saliency map; Visual attention

Global feature integration based salient region detection

Salient instance segmentation via subitizing and clustering

Finding most striking region in an image is known as salient region detection. This area is becoming an area of research in recent years due to its wide applicability in computer vision, robotics and data transmission. To find salient region various parameters like color, texture, location, semantics etc are used. In this paper, a method is proposed which uses the fact that image boundary is rarely touched by salient object and then poisson distribution is used to find the probability of each pixel being part of salient object. Thus, the saliency map is obtained by computing the absolute difference of pixel intensity with the mean of boundary pixels. No former training is essential for this approach. Experiments are performed to evaluate the performance of proposed approach on MSRA dataset and compared with 4 recent state-of-arts. Precision, recall and F-measure proves the consistency of proposed work.

Event Abstract Back to Event Can state-of-the-art saliency systems model infant gazing behavior in tutoring situations? Vikram Tadmeri Narayan1*, Katrin S. Lohan1, Marko Tscherepanow1, Katharina Rohlfing1 and Britta Wrede1 1 Bielefeld University, Applied Informatics Group, CoR-Lab, Germany I. I NTRODUCTION The behavior for a humanoid robot is often modeled in accordance with human behavior. Current research suggests that analyzing infant behavior as a basis for designing the robot behavior can guide us to a natural robot interface. Based on this idea many researchers support saliency systems as a bottom-up inspired way to simulate infant-like gazing behavior. In the field of saliency systems many different approaches have proposed and quantified in terms of speed, quality and other technical issues. But so far, no one compared and quantified them in terms of natural infant tutor interaction. The question we would like to address in this paper is: Can state-of-the-art saliency systems model infant gazing behavior in tutoring situations? By addressing these issues we want to take a step towards an autonomous robot system, which could be used more natural interaction experiments in future. II. M ETHODS FOR A NALYZING We compared 7 different saliency systems with the gazing behavior of infants between the age of 8-11 months. The infant’s gazing behavior and the interaction structure of the tutoring situation was manually annotated. Based on this annotation we selected images as input for the saliency systems. The interaction data used in the course of this research is described in the following subsections. The employed saliency systems are briefly described in subsection II-B. A. Parent-child interaction Out of the well-known Bielefeld-motionese corpus [8], we focus on the youngest participants comprising 12 families of 8 to 11 months old children to analyze their gazing behavior, as the main feedback and controlling capabilities of these infants is based on the gazing behavior [13]. We selected the stacking cups task, because it was often analyzed [7],[12] and the interaction is very detailed analysed. We selected several pictures based on an action segmentation. The pictures represent the beginning and ending point of the stacking cups task. We have chosen this points because at these points the scene is changing. Setting: Parents were instructed to demonstrate a stacking cups task to an interaction partner. The first interaction partner was their infant, and another adult constituted the second. Fig. 1 illustrates the top-view of the experimental setup, and shows sample image frames of cameras which were set behind the parent and the interaction partner and focused on each of them. The stacking cups task was to sequentially pick up the green (a1), the yellow (a2), and the red (a3) cup and put them into the blue one on the white tray. B. Saliency Systems Frequency tuned saliency model [1] is perhaps the simplest of all the existing saliency systems. The absolute difference of each pixel to the image mean is accounted as its saliency value. They recommend to decompose a given input image into L*, a*, b* color space and perform the aforementioned operation on each of the color plane and fuse the results to compute the final saliency map. Graph-based visual saliency models [4] envisages the input image as a complete graph with each pixel as its nodes. A Weber’s [3] law based dissimilarity measure is employed to compute the dissimilarity between any two given pixels. A normalizing function is further employed to compute the final saliency map. Experiments carried out in [4], [10] have shown that the method has high performance in correlating with human eye gaze and also for object segmentation. The Multi resolution saliency map [5] is perhaps the most cited saliency system to date. The authors propose a computational model for the theoretical framework presented by Koch and Ulman [6] to model visual attention. An input image is analyzed with opponent color maps, pixels gradient and orientation at different scale spaces. A winner take all (WTA) network fuses these multiple maps into a single saliency map. Local steering kernel-based saliency [9] is a based on the center-surround paradigm, which is employed to compute the saliency of a selected region. They propose a novel local steering kernel which is employed to compute the similarity between a center region and the surrounding patches. The methodology is inherently robust to image brightness and contrast changes, has very few parameters that requires to be fined-tuned. Symmetry based Saliency [2] is compute rectangular regions of interest centered on a pixel and compute first order moments of features to calculate the saliency of the given pixel. The methodology is among the most best in terms of programming simplicity. The authors also claim the biological plausibility of the model. Random center-surround pattern based Saliency [11] is a methodology that employs a biologically plausible dissimilarity metric which is employed to compute the contrast between any two random pixels on the input image. The contrasts are updated and normalized to generate the final saliency map. The said methodology is shown to have state-of the-art performance in the task of salient region detection. For the Random rectangular regions of interest based saliency [10], the same authors as in [11] propose to compute frequent- tuned salient region detection [1], on random rectangular regions of an image for a large number of times and then sum them to compute the final saliency map. Such a formulation is shown to have excellent correlation with human eye-gaze and has good performance for the task of salient regions as shown in their experiments. The methodology also has only two parameters which requires fine tuning, which reduces the implementation complexity. III. DATA The beginning and concluding snapshots of 12 motionese videos were cropped, and were parsed into the seven aforementioned saliency systems. A 15 x 15 region centered on the maximally salient point was chosen as the focus of attention. see Fig. 1. Results: Results are summarized in Fig. 2. The percentages describes the matching accuracy of a given saliency system to the child’s eye gazing behavior. It seems that the methodology of Seo and Milanfar [9] has the best matching with the gazing behavior of a 8-11 month old child. Figure 1 Figure 2 Acknowledgements Tadmeri Narayan Vikram gratefully acknowledges the financial support from the EU FP7 Marie Curie ITN RobotDoc. Contract No. 235065. Katrin S. Lohan, Katharina Rohlfing, and Britta Wrede gratefully acknowledge the financial support from the FP7 European Project ITALK.

We propose a simple yet effective structure-guided statistical textural distinctiveness approach to salient region detection. Our method uses a multilayer approach to analyze the structural and textural characteristics of natural images as important features for salient region detection from a scale point of view. To represent the structural characteristics, we abstract the image using structured image elements and extract rotational-invariant neighborhood-based textural representations to characterize each element by an individual texture pattern. We then learn a set of representative texture atoms for sparse texture modeling and construct a statistical textural distinctiveness matrix to determine the distinctiveness between all representative texture atom pairs in each layer. Finally, we determine saliency maps for each layer based on the occurrence probability of the texture atoms and their respective statistical textural distinctiveness and fuse them to compute a final saliency map. Experimental results using four public data sets and a variety of performance evaluation metrics show that our approach provides promising results when compared with existing salient region detection approaches.

Selective Visual Attention Model (SVAM) plays an important role in region-based image retrieval. In this paper, a robust and accurate method for salient region detection is proposed which integrates SVAM and image segmentation. After that, the concept of salient region adjacency graphs (SRAGs) is introduced for image retrieval. The whole process consists of three levels. First in the pixel-level, the salient value of each pixel is calculated using an improved spatial-based attention model. Then in the region-level, the salient region detection method is presented. Furthermore, in the scene-level, salient region adjacency graphs (SRAGs) are introduced to represent the salient groups in the image, which take the salient regions as root nodes. Finally, the constructed SRAGs are used for image retrieval. Experiments show that the proposed method works well.KeywordsRoot NodeImage RetrievalSalient RegionImage Segmentation MethodAttention WindowThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Visual saliency guided video compression algorithm

The objective of this paper is twofold. First, we introduce an effective region-based solution for saliency detection. Then, we apply the achieved saliency map to better encode the image features for solving object recognition task. To find the perceptually and semantically meaningful salient regions, we extract superpixels based on an adaptive mean shift algorithm as the basic elements for saliency detection. The saliency of each superpixel is measured by using its spatial compactness, which is calculated according to the results of Gaussian mixture model (GMM) clustering. To propagate saliency between similar clusters, we adopt a modified PageRank algorithm to refine the saliency map. Our method not only improves saliency detection through large salient region detection and noise tolerance in messy background, but also generates saliency maps with a well-defined object shape. Experimental results demonstrate the effectiveness of our method. Since the objects usually correspond to salient regions, and these regions usually play more important roles for object recognition than background, we apply our achieved saliency map for object recognition by incorporating a saliency map into sparse coding-based spatial pyramid matching (ScSPM) image representation. To learn a more discriminative codebook and better encode the features corresponding to the patches of the objects, we propose a weighted sparse coding for feature coding. Moreover, we also propose a saliency weighted max pooling to further emphasize the importance of those salient regions in feature pooling module. Experimental results on several datasets illustrate that our weighted ScSPM framework greatly outperforms ScSPM framework, and achieves excellent performance for object recognition.

Salient region detection is useful for several image-processing applications, such as adaptive compression, object recognition, image retrieval, filter design, and image retargeting. A novel method to determine the salient regions of images is proposed in this paper. The L₀ smoothing filter and principle component analysis (PCA) play important roles in our framework. The L₀ filter is extremely helpful in characterizing fundamental image constituents, i.e., salient edges, and can simultaneously diminish insignificant details, thus producing more accurate boundary information for background merging and boundary scoring. PCA can reduce computational complexity as well as attenuate noise and translation errors. A local-global contrast is then used to calculate the distinction. Finally, image segmentation is used to achieve full-resolution saliency maps. The proposed method is compared with other state-of-the-art saliency detection methods and shown to yield higher precision-recall rates and F-measures.

Automatic detection of salient image regions is a useful tool with applications in intelligent camera control, virtual cinematography, video summarization and editing, evaluation of viewer preferences, and many others. This paper presents an effective method for detecting potentially salient foreground regions. Salient regions are identified by eigenvalue analysis of a graph Laplacian that is defined over the color similarity of image superpixels, under the assumption that the majority of pixels on the image boundary show non-salient background. In contrast to previous methods based on graph-cuts or graph partitioning, our method provides continuously-valued saliency estimates with complementary properties to recently proposed color contrast-based approaches. Moreover, exploiting discriminative properties of the Fiedler vector, we devise an SVM-based classifier that allows us to determine whether an image contains any salient objects at all, a problem that has been largely neglected in previous works. We also describe how the per-frame saliency detection can be extended to improve its spatiotemporal coherence when computed on video sequences. Extensive evaluation on several datasets demonstrates and validates the state-of-the-art performance of the proposed method.

Salient region detection is to uniformly locate interest regions or objects in an image. It is a hot topic in computer vision, and has a wide range of applications like object recognition and segmentation. Although considerable progress has been made, salient region detection remains a challenging issue. In this paper we present a simple yet effective salient region detection approach by integrating spatial and background priors. By considering spatial priors which include spatial distribution of color similarity and center-bias, the background regions and foreground regions are extracted preliminarily. Based on the extracted background we use the background prior to suppress the background regions more sufficiently. Experimental results on public benchmark databases show that the proposed approach can effectively locate salient object regions with well-defined boundaries and suppress background regions.

In this article, we propose a simple and efficient method for computing an image saliency map, which performs well on both salient region detection and as well as eye gaze prediction tasks. A large number of distinct sub-windows with random co-ordinates and scales are generated over an image. The saliency descriptor of a pixel within a random sub-window is given by the absolute difference of its intensity value to the mean intensity of the sub-window. The final saliency value of a given pixel is obtained as the sum of all saliency descriptors corresponding to this pixel. Any given pixel can be included by one or more random sub-windows. The recall-precision performance of the proposed saliency map is comparable to other existing saliency maps for the task of salient region detection. It also achieves state-of-the-art performance for the task of eye gaze prediction in terms of receiver operating characteristics.KeywordsBottom-up Visual AttentionSaliency MapSalient Region DetectionEye Fixation

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.