How best to evaluate a saliency model’s ability to predict where humans look in images is an open research question. The choice of evaluation metric depends on how saliency is defined and how the ground truth is represented. Metrics differ in how they rank saliency models, and this results from how false positives and false negatives are treated, whether viewing biases are accounted for, whethe...

Human eyes can identify person identities based on small salient regions, i.e. person saliency is distinctive and reliable. Saliency relates to matching regions with attributes that make a person distinctive and are useful in finding the same person across camera views. Person re-identification with saliency learning can be applied in human tracking, surveillance , retrieval etc.

The majority of visual attention models is based on the concept of saliency map, a two-dimensional map that encodes the saliency of objects in the surrounding world. We attempt a short review of current implementations and present our first thoughts on extending the classical saliency-based model by using motion and prior knowledge.

Visual tracking in aerial videos is a challenging task in computer vision and remote sensing technologies due to appearance variation difficulties. Appearance variations are caused by camera and target motion, low resolution noisy images, scale changes, and pose variations. Various approaches have been proposed to deal with appearance variation difficulties in aerial videos, and amongst these m...

Motion saliency map is a critical feature in rapid analysis of video data, especially in coping with visual information overload and cluttered background [1]. Its potential applications include automatic action recognition, recognition of moving objects, and target tracking [2, 3, 4]. Similar to the other feature saliency maps, a motion saliency map is a topographic representation of the motion...

A novel mechanism to simulate visual attention mechanisms for content-based image retrieval, based on saliency structure histogram method was proposed in this paper. In CBIR, images are indexed by their visual content, such as color, texture, shapes. A color volume with edge information together is used to detect saliency regions. The texture image features, such as energy, inverse difference m...

The prediction of saliency areas in images has been traditionally addressed with hand crafted features based on neuroscience principles. This paper however addreses the problem with a completely data-driven approach by training a convolutional network. The learning process is formulated as a minimization of a loss function that measures the Euclidean distance of the predicted saliency map with ...

This paper proposes a novel method for visual saliency detection based on an universal probabilistic model, which measures the saliency by combining low level features and location prior. We view the task of estimating visual saliency as searching the most conspicuous parts in an image and extract the saliency map by computing the dissimilarity between different regions. We simulate the moving ...

In this paper we address the problem of obtaining meaningful saliency measures that tie in coherently with other methods and modalities within larger robotic systems. We learn probabilistic models of various saliency cues from labeled training data and fuse these into probability maps, which while appearing to be qualitatively similar to traditional saliency maps, represent actual probabilities...

Many decisions we make require visually identifying and evaluating numerous alternatives quickly. These usually vary in reward, or value, and in low-level visual properties, such as saliency. Both saliency and value influence the final decision. In particular, saliency affects fixation locations and durations, which are predictive of choices. However, it is unknown how saliency propagates to th...