# Visual Saliency Detection Using a Rule-Based Aggregation Approach  
[PERSON]  
1 Departamento de Ingenieria Electronica, DICIS, Universidad de Guanajuato, Salamanca 36885, Mexico; [EMAIL_ADDRESS] (A.L.-A. ); [EMAIL_ADDRESS] (R.E.S.-Y. ); [EMAIL_ADDRESS] (D.E.M.-R.); [EMAIL_ADDRESS] (M.A.C.-C.)  
2 Departamento Arte y Empresa, DICIS, Universidad de Guanajuato, Salamanca 36885, Mexico
models that make use of intrinsic information from the image to perform a saliency prediction task, i.e., using mainly low-level features such as color or contrast, without any prior knowledge about the image. Generally, individual models use biological theories, purely computational formulations, or a combination of both [15]. The first individual model is the seminal work presented by [PERSON] et al. [16]. In his research work, [PERSON] estimated the saliency of a given pixel incorporating the cognitive theory presented by [PERSON] and [PERSON] [17], which suggests that the human visual system responds to contrast stimulus in color, orientation, and intensity. In addition, this model takes into account the computational architecture proposed by [PERSON] and [PERSON] [1]. From this work onwards, many approaches have been proposed based on the same cognitive assumptions. As examples of this type of approach, we can mention the proposals by [PERSON] et al. [18; 19], [PERSON] et al. [20], and [PERSON] et al. [21]. Additionally, other individual models came up to the scene taking into account not only pixel-level information but also regional and global cues. As an example of these types of models, we can mention the approach proposed by [PERSON] et al. [15] where the saliency is estimated as the difference between the pixel color and the color average of the image. In the research work of [PERSON] et al. [22], two approaches are introduced. The first approach computes saliency as the distance in color between pixels. The second approach considers saliency as the weighted color distance between regions. [PERSON] et al. [23] proposed a method for saliency detection based on feature contrast, where uniqueness and spatial distribution of regions into the image are computed using a high dimensional Gaussian filter. Based on the concept that salient regions are distinctive to their local and global surrounding, [PERSON] et al. [24] proposed their context-aware saliency detection model. Recently, [PERSON] et al. [25] proposed a model which considers global contrast in different directions for each pixel in the CIELAB color space. The main goal of a saliency detection model is to estimate saliency in a scene, determining if a location of a given image is salient or not. Usually, the saliency information is encoded in a saliency map (SM) which is a two-dimensional representation given in grayscale [26]. In the SM, the clearest location corresponds to the most salient region and the darkest location to the less salient region. If we think of the saliency map as a binary classifier [27], where we represent whether a location of an image is salient or not, then the individual models offer only partial results. The outcomes from individual models need post-processing such as thresholding or segmentation to obtain a binary saliency map. To combine to the best characteristics of individual models, the second category of models is proposed: aggregation and learning models. Aggregation models or learning techniques, attempt to combine in different manners the outcomes of individual saliency detection models in order to obtain a more robust result. Several direct combination techniques have been proposed to overcome the deficiencies of individual approaches, such as in the research work of [PERSON] et al. [14]. In that work, [PERSON] presented four combination techniques for saliency aggregation from a probabilistic point of view. Such techniques are Naive Bayesian evidence accumulation and linear summation of identity, exponential, and logarithm functions.