Saliency-based identification and recognition of pointed-at objects

Abstract — When persons interact, non-verbal cues are used to direct the attention of persons towards objects of interest. Achieving joint attention this way is an important aspect of natural communication. Most importantly, it allows to couple verbal descriptions with the visual appearance of objects, if the referred-to object is non-verbally indicated. In this contri-bution, we present a system that utilizes bottom-up saliency and pointing gestures to efficiently identify pointed-at objects. Furthermore, the system focuses the visual attention by steering a pan-tilt-zoom camera towards the object of interest and thus provides a suitable model-view for SIFT-based recognition and learning. We demonstrate the practical applicability of the proposed system through experimental evaluation in different environments with multiple pointers and objects

Visual Saliency Based on Multiscale Deep Features

Visual saliency is a fundamental problem in both cognitive and computational sciences, including computer vision. In this CVPR 2015 paper, we discover that a high-quality visual saliency model can be trained with multiscale features extracted using a popular deep learning architecture, convolutional neural networks (CNNs), which have had many successes in visual recognition tasks. For learning such saliency models, we introduce a neural network architecture, which has fully connected layers on top of CNNs responsible for extracting features at three different scales. We then propose a refinement method to enhance the spatial coherence of our saliency results. Finally, aggregating multiple saliency maps computed for different levels of image segmentation can further boost the performance, yielding saliency maps better than those generated from a single segmentation. To promote further research and evaluation of visual saliency models, we also construct a new large database of 4447 challenging images and their pixelwise saliency annotation. Experimental results demonstrate that our proposed method is capable of achieving state-of-the-art performance on all public benchmarks, improving the F-Measure by 5.0% and 13.2% respectively on the MSRA-B dataset and our new dataset (HKU-IS), and lowering the mean absolute error by 5.7% and 35.1% respectively on these two datasets.Comment: To appear in CVPR 201

Action comprehension: deriving spatial and functional relations.

A perceived action can be understood only when information about the action carried out and the objects used are taken into account. It was investigated how spatial and functional information contributes to establishing these relations. Participants observed static frames showing a hand wielding an instrument and a potential target object of the action. The 2 elements could either match or mismatch, spatially or functionally. Participants were required to judge only 1 of the 2 relations while ignoring the other. Both irrelevant spatial and functional mismatches affected judgments of the relevant relation. Moreover, the functional relation provided a context for the judgment of the spatial relation but not vice versa. The results are discussed in respect to recent accounts of action understanding

Rapid Visual Categorization is not Guided by Early Salience-Based Selection

The current dominant visual processing paradigm in both human and machine research is the feedforward, layered hierarchy of neural-like processing elements. Within this paradigm, visual saliency is seen by many to have a specific role, namely that of early selection. Early selection is thought to enable very fast visual performance by limiting processing to only the most salient candidate portions of an image. This strategy has led to a plethora of saliency algorithms that have indeed improved processing time efficiency in machine algorithms, which in turn have strengthened the suggestion that human vision also employs a similar early selection strategy. However, at least one set of critical tests of this idea has never been performed with respect to the role of early selection in human vision. How would the best of the current saliency models perform on the stimuli used by experimentalists who first provided evidence for this visual processing paradigm? Would the algorithms really provide correct candidate sub-images to enable fast categorization on those same images? Do humans really need this early selection for their impressive performance? Here, we report on a new series of tests of these questions whose results suggest that it is quite unlikely that such an early selection process has any role in human rapid visual categorization.Comment: 22 pages, 9 figure