Visual search and decision making in bees: time, speed and accuracy

An insect searching a meadow for flowers may detect several flowers from different species per second, so the task of choosing the right flowers rapidly is not trivial. Here we apply concepts from the field of visual search in human experimental psychology to the task a bee faces in searching a meadow for familiar flowers, and avoiding ‘‘distraction’’ by unknown or unrewarding flowers. Our approach highlights the importance of visual information processing for understanding the behavioral ecology of foraging. Intensity of illuminating light, target contrast with background (both chromatic and achromatic), and number of distractors are all shown to have a direct influence on decision times in behavioral choice experiments. To a considerable extent, the observed search behavior can be explained by the temporal and spatial properties of neuronal circuits underlying visual object detection. Our results also emphasize the importance of the time dimension in decision making. During visual search in humans, improved accuracy in solving discrimination tasks comes at a cost in response time, but the vast majority of studies on decision making in animals have focused on choice accuracy, not speed. We show that in behavioral choice experiments in bees, there is a tight link between the two. We demonstrate both between-individual and within- individual speed-accuracy tradeoffs, whereby bees exhibit considerable behavioral flexibility in solving visual search tasks. Motivation is an important factor in selection of behavioral strategies for a search task, and sensory discrimination capabilities may be underestimated by studies that quantify accuracy of behavioral choice but neglect the temporal dimension

Cognitive dimensions of predator responses to imperfect mimicry?

Many palatable insects, for example hoverflies, deter predators by mimicking well-defended insects such as wasps. However, for human observers, these flies often seem to be little better than caricatures of wasps – their visual appearance and behaviour are easily distinguishable. This imperfect mimicry baffles evolutionary biologists, because one might expect natural selection to do a more thorough job. Here we discuss two types of cognitive processes that might explain why mimics distinguishable mimics might enjoy increased protection from predation. Speed accuracy tradeoffs in predator decision making might give imperfect mimics sufficient time to escape, and predators under time constraint might avoid time-consuming discriminations between well-defended models and inaccurate edible mimics, and instead adopt a “safety first” policy of avoiding insects with similar appearance. Categorization of prey types by predators could mean that wholly dissimilar mimics may be protected, provided they share some common property with noxious prey

Physical Representation-based Predicate Optimization for a Visual Analytics Database

Querying the content of images, video, and other non-textual data sources requires expensive content extraction methods. Modern extraction techniques are based on deep convolutional neural networks (CNNs) and can classify objects within images with astounding accuracy. Unfortunately, these methods are slow: processing a single image can take about 10 milliseconds on modern GPU-based hardware. As massive video libraries become ubiquitous, running a content-based query over millions of video frames is prohibitive. One promising approach to reduce the runtime cost of queries of visual content is to use a hierarchical model, such as a cascade, where simple cases are handled by an inexpensive classifier. Prior work has sought to design cascades that optimize the computational cost of inference by, for example, using smaller CNNs. However, we observe that there are critical factors besides the inference time that dramatically impact the overall query time. Notably, by treating the physical representation of the input image as part of our query optimization---that is, by including image transforms, such as resolution scaling or color-depth reduction, within the cascade---we can optimize data handling costs and enable drastically more efficient classifier cascades. In this paper, we propose Tahoma, which generates and evaluates many potential classifier cascades that jointly optimize the CNN architecture and input data representation. Our experiments on a subset of ImageNet show that Tahoma's input transformations speed up cascades by up to 35 times. We also find up to a 98x speedup over the ResNet50 classifier with no loss in accuracy, and a 280x speedup if some accuracy is sacrificed.Comment: Camera-ready version of the paper submitted to ICDE 2019, In Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE 2019

Essential guidelines for computational method benchmarking

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology.Comment: Minor update

Essential guidelines for computational method benchmarking

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology

Efficient Nearest Neighbor Classification Using a Cascade of Approximate Similarity Measures

Nearest neighbor classification using shape context can yield highly accurate results in a number of recognition problems. Unfortunately, the approach can be too slow for practical applications, and thus approximation strategies are needed to make shape context practical. This paper proposes a method for efficient and accurate nearest neighbor classification in non-Euclidean spaces, such as the space induced by the shape context measure. First, a method is introduced for constructing a Euclidean embedding that is optimized for nearest neighbor classification accuracy. Using that embedding, multiple approximations of the underlying non-Euclidean similarity measure are obtained, at different levels of accuracy and efficiency. The approximations are automatically combined to form a cascade classifier, which applies the slower approximations only to the hardest cases. Unlike typical cascade-of-classifiers approaches, that are applied to binary classification problems, our method constructs a cascade for a multiclass problem. Experiments with a standard shape data set indicate that a two-to-three order of magnitude speed up is gained over the standard shape context classifier, with minimal losses in classification accuracy.National Science Foundation (IIS-0308213, IIS-0329009, EIA-0202067); Office of Naval Research (N00014-03-1-0108