Multiscale Discriminant Saliency for Visual Attention

The bottom-up saliency, an early stage of humans' visual attention, can be considered as a binary classification problem between center and surround classes. Discriminant power of features for the classification is measured as mutual information between features and two classes distribution. The estimated discrepancy of two feature classes very much depends on considered scale levels; then, multi-scale structure and discriminant power are integrated by employing discrete wavelet features and Hidden markov tree (HMT). With wavelet coefficients and Hidden Markov Tree parameters, quad-tree like label structures are constructed and utilized in maximum a posterior probability (MAP) of hidden class variables at corresponding dyadic sub-squares. Then, saliency value for each dyadic square at each scale level is computed with discriminant power principle and the MAP. Finally, across multiple scales is integrated the final saliency map by an information maximization rule. Both standard quantitative tools such as NSS, LCC, AUC and qualitative assessments are used for evaluating the proposed multiscale discriminant saliency method (MDIS) against the well-know information-based saliency method AIM on its Bruce Database wity eye-tracking data. Simulation results are presented and analyzed to verify the validity of MDIS as well as point out its disadvantages for further research direction.Comment: 16 pages, ICCSA 2013 - BIOCA sessio

Multi-scale Discriminant Saliency with Wavelet-based Hidden Markov Tree Modelling

The bottom-up saliency, an early stage of humans' visual attention, can be considered as a binary classification problem between centre and surround classes. Discriminant power of features for the classification is measured as mutual information between distributions of image features and corresponding classes . As the estimated discrepancy very much depends on considered scale level, multi-scale structure and discriminant power are integrated by employing discrete wavelet features and Hidden Markov Tree (HMT). With wavelet coefficients and Hidden Markov Tree parameters, quad-tree like label structures are constructed and utilized in maximum a posterior probability (MAP) of hidden class variables at corresponding dyadic sub-squares. Then, a saliency value for each square block at each scale level is computed with discriminant power principle. Finally, across multiple scales is integrated the final saliency map by an information maximization rule. Both standard quantitative tools such as NSS, LCC, AUC and qualitative assessments are used for evaluating the proposed multi-scale discriminant saliency (MDIS) method against the well-know information based approach AIM on its released image collection with eye-tracking data. Simulation results are presented and analysed to verify the validity of MDIS as well as point out its limitation for further research direction.Comment: arXiv admin note: substantial text overlap with arXiv:1301.396

A computational visual saliency model for images.

Human eyes receive an enormous amount of information from the visual world. It is highly difficult to simultaneously process this excessive information for the human brain. Hence the human visual system will selectively process the incoming information by attending only the relevant regions of interest in a scene. Visual saliency characterises some parts of a scene that appears to stand out from its neighbouring regions and attracts the human gaze. Modelling saliency-based visual attention has been an active research area in recent years. Saliency models have found vital importance in many areas of computer vision tasks such as image and video compression, object segmentation, target tracking, remote sensing and robotics. Many of these applications deal with high-resolution images and real-time videos and it is a challenge to process this excessive amount of information with limited computational resources. Employing saliency models in these applications will limit the processing of irrelevant information and further will improve their efficiency and performance. Therefore, a saliency model with good prediction accuracy and low computation time is highly essential. This thesis presents a low-computation wavelet-based visual saliency model designed to predict the regions of human eye fixations in images. The proposed model uses two-channel information luminance (Y) and chrominance (Cr) in YCbCr colour space for saliency computation. These two channels are decomposed to their lowest resolution using two-dimensional Discrete Wavelet Transform (DWT) to extract the local contrast features at multiple scales. The extracted local contrast features are integrated at multiple levels using a two-dimensional entropy-based feature combination scheme to derive a combined map. The combined map is normalized and enhanced using natural logarithm transformation to derive a final saliency map. The performance of the model has been evaluated qualitatively and quantitatively using two large benchmark image datasets. The experimental results show that the proposed model has achieved better prediction accuracy both qualitatively and quantitatively with a significant reduction in computation time when compared to the existing benchmark models. It has achieved nearly 25% computational savings when compared to the benchmark model with the lowest computation time

Visual Saliency Based on Fast Nonparametric Multidimensional Entropy Estimation

Bottom-up visual saliency can be computed through information theoretic models but existing methods face significant computational challenges. Whilst nonparametric methods suffer from the curse of dimensionality problem and are computationally expensive, parametric approaches have the difficulty of determining the shape parameters of the distribution models. This paper makes two contributions to information theoretic based visual saliency models. First, we formulate visual saliency as center surround conditional entropy which gives a direct and intuitive interpretation of the center surround mechanism under the information theoretic framework. Second, and more importantly, we introduce a fast nonparametric multidimensional entropy estimation solution to make information theoretic-based saliency models computationally tractable and practicable in realtime applications. We present experimental results on publicly available eyetracking image databases to demonstrate that the proposed method is competitive to state of the art

Multi-focus image fusion using maximum symmetric surround saliency detection

In digital photography, two or more objects of a scene cannot be focused at the same time. If we focus one object, we may lose information about other objects and vice versa. Multi-focus image fusion is a process of generating an all-in-focus image from several out-of-focus images. In this paper, we propose a new multi-focus image fusion method based on two-scale image decomposition and saliency detection using maximum symmetric surround. This method is very beneficial because the saliency map used in this method can highlight the saliency information present in the source images with well defined boundaries. A weight map construction method based on saliency information is developed in this paper. This weight map can identify the focus and defocus regions present in the image very well. So we implemented a new fusion algorithm based on weight map which integrate only focused region information into the fused image. Unlike multi-scale image fusion methods, in this method two-scale image decomposition is sufficient. So, it is computationally efficient. Proposed method is tested on several multi-focus image datasets and it is compared with traditional and recently proposed fusion methods using various fusion metrics. Results justify that our proposed method gives stable and promising performance when compared to that of the existing methods

A Decision Support System (DSS) for Breast Cancer Detection Based on Invariant Feature Extraction, Classification, and Retrieval of Masses of Mammographic Images

This paper presents an integrated system for the breast cancer detection from mammograms based on automated mass detection, classification, and retrieval with a goal to support decision-making by retrieving and displaying the relevant past cases as well as predicting the images as benign or malignant. It is hypothesized that the proposed diagnostic aid would refresh the radiologist’s mental memory to guide them to a precise diagnosis with concrete visualizations instead of only suggesting a second diagnosis like many other CAD systems. Towards achieving this goal, a Graph-Based Visual Saliency (GBVS) method is used for automatic mass detection, invariant features are extracted based on using Non-Subsampled Contourlet transform (NSCT) and eigenvalues of the Hessian matrix in a histogram of oriented gradients (HOG), and finally classification and retrieval are performed based on using Support Vector Machines (SVM) and Extreme Learning Machines (ELM), and a linear combination-based similarity fusion approach. The image retrieval and classification performances are evaluated and compared in the benchmark Digital Database for Screening Mammography (DDSM) of 2604 cases by using both the precision-recall and classification accuracies. Experimental results demonstrate the effectiveness of the proposed system and show the viability of a real-time clinical application