Some Objects Are More Equal Than Others: Measuring and Predicting Importance

We observe that everyday images contain dozens of objects, and that humans, in describing these images, give different priority to these objects. We argue that a goal of visual recognition is, therefore, not only to detect and classify objects but also to associate with each a level of priority which we call 'importance'. We propose a definition of importance and show how this may be estimated reliably from data harvested from human observers. We conclude by showing that a first-order estimate of importance may be computed from a number of simple image region measurements and does not require access to image meaning

View and Illumination Invariant Object Classification Based on 3D Color Histogram Using Convolutional Neural Networks

Object classification is an important step in visual recognition and semantic analysis of visual content. In this paper, we propose a method for classification of objects that is invariant to illumination color, illumination direction and viewpoint based on 3D color histogram. A 3D color histogram of an image is represented as a 2D image, to capture the color composition while preserving the neighborhood information of color bins, to realize the necessary visual cues for classification of objects. Also, the ability of convolutional neural network (CNN) to learn invariant visual patterns is exploited for object classification. The efficacy of the proposed method is demonstrated on Amsterdam Library of Object Images (ALOI) dataset captured under various illumination conditions and angles-of-view

Dynamic cooling strategy based on individual animal response mitigated heat stress in dairy cows

Technological progress enables individual cow's temperatures to be measured in real time, using a bolus sensor inserted into the rumen (reticulorumen). However, current cooling systems often work at a constant schedule based on the ambient temperature and not on monitoring the animal itself. This study hypothesized that tailoring the cooling management to the cow's thermal reaction can mitigate heat stress. We propose a dynamic cooling system based on in vivo temperature sensors (boluses). Thus, cooling can be activated as needed and is thus most efficacious. A total of 30 lactating cows were randomly assigned to one of two groups; the groups received two different evaporative cooling regimes. A control group received cooling sessions on a preset time-based schedule, the method commonly used in farms; and an experimental group, which received the sensor-based (SB) cooling regime. Sensor-based was changed weekly according to the cow's reaction, as reflected in the changes in body temperatures from the previous week, as measured by reticulorumen boluses. The two treatment groups of cows had similar milk yields (44.7 kg/d), but those in the experimental group had higher milk fat (3.65 vs 3.43%), higher milk protein (3.23 vs 3.13%), higher energy corrected milk (ECM, 42.84 vs 41.48 kg/d), higher fat corrected milk 4%; (42.76 vs 41.34 kg/d), and shorter heat stress duration (5.03 vs 9.46 h/day) comparing to the control. Dry matter intake was higher in the experimental group. Daily visits to the feed trough were less frequent, with each visit lasting longer. The sensor-based cooling regime may be an effective tool to detect and ease heat stress in high-producing dairy cows during transitional seasons when heat load can become severe in arid and semi-arid zones

Dynamic cooling strategy based on individual animal response mitigated heat stress in dairy cows

Technological progress enables individual cow's temperatures to be measured in real time, using a bolus sensor inserted into the rumen (reticulorumen). However, current cooling systems often work at a constant schedule based on the ambient temperature and not on monitoring the animal itself. This study hypothesized that tailoring the cooling management to the cow's thermal reaction can mitigate heat stress. We propose a dynamic cooling system based on in vivo temperature sensors (boluses). Thus, cooling can be activated as needed and is thus most efficacious. A total of 30 lactating cows were randomly assigned to one of two groups; the groups received two different evaporative cooling regimes. A control group received cooling sessions on a preset time-based schedule, the method commonly used in farms; and an experimental group, which received the sensor-based (SB) cooling regime. Sensor-based was changed weekly according to the cow's reaction, as reflected in the changes in body temperatures from the previous week, as measured by reticulorumen boluses. The two treatment groups of cows had similar milk yields (44.7 kg/d), but those in the experimental group had higher milk fat (3.65 vs 3.43%), higher milk protein (3.23 vs 3.13%), higher energy corrected milk (ECM, 42.84 vs 41.48 kg/d), higher fat corrected milk 4%; (42.76 vs 41.34 kg/d), and shorter heat stress duration (5.03 vs 9.46 h/day) comparing to the control. Dry matter intake was higher in the experimental group. Daily visits to the feed trough were less frequent, with each visit lasting longer. The sensor-based cooling regime may be an effective tool to detect and ease heat stress in high-producing dairy cows during transitional seasons when heat load can become severe in arid and semi-arid zones

Measures and Limits of Models of Fixation Selection

Models of fixation selection are a central tool in the quest to understand how the human mind selects relevant information. Using this tool in the evaluation of competing claims often requires comparing different models' relative performance in predicting eye movements. However, studies use a wide variety of performance measures with markedly different properties, which makes a comparison difficult. We make three main contributions to this line of research: First we argue for a set of desirable properties, review commonly used measures, and conclude that no single measure unites all desirable properties. However the area under the ROC curve (a classification measure) and the KL-divergence (a distance measure of probability distributions) combine many desirable properties and allow a meaningful comparison of critical model performance. We give an analytical proof of the linearity of the ROC measure with respect to averaging over subjects and demonstrate an appropriate correction of entropy-based measures like KL-divergence for small sample sizes in the context of eye-tracking data. Second, we provide a lower bound and an upper bound of these measures, based on image-independent properties of fixation data and between subject consistency respectively. Based on these bounds it is possible to give a reference frame to judge the predictive power of a model of fixation selection . We provide open-source python code to compute the reference frame. Third, we show that the upper, between subject consistency bound holds only for models that predict averages of subject populations. Departing from this we show that incorporating subject-specific viewing behavior can generate predictions which surpass that upper bound. Taken together, these findings lay out the required information that allow a well-founded judgment of the quality of any model of fixation selection and should therefore be reported when a new model is introduced

Oculomotor Evidence for Top-Down Control following the Initial Saccade

The goal of the current study was to investigate how salience-driven and goal-driven processes unfold during visual search over multiple eye movements. Eye movements were recorded while observers searched for a target, which was located on (Experiment 1) or defined as (Experiment 2) a specific orientation singleton. This singleton could either be the most, medium, or least salient element in the display. Results were analyzed as a function of response time separately for initial and second eye movements. Irrespective of the search task, initial saccades elicited shortly after the onset of the search display were primarily salience-driven whereas initial saccades elicited after approximately 250 ms were completely unaffected by salience. Initial saccades were increasingly guided in line with task requirements with increasing response times. Second saccades were completely unaffected by salience and were consistently goal-driven, irrespective of response time. These results suggest that stimulus-salience affects the visual system only briefly after a visual image enters the brain and has no effect thereafter