Salvador Ruiz-Correa

Extensive partition operators, gray-level connected operators, and region merging/classification segmentation algorithms: theoretical links

The relation between morphological gray-level connected operators and segmentation algorithms based on region merging/classification strategies has been pointed out several times in the literature. However, to the best of our knowledge, the formal relation between them has not been established. This paper presents the link between the two domains based on the observation that both connected operators and segmentation algorithms share a key mechanism: they simultaneously operate on images and on partitions, and therefore they can be described as operations on a joint image-partition model. As a result, we analyze both segmentation algorithms and connected operators by defining operators on complete product lattices, that explicitly model gray-level and partition attributes. In the first place, starting with a complete lattice of partitions, we initially define the concept of the segmentation model as a mapping in a product lattice, whose elements are three-tuples consisting of a partition, an image that models the partition attributes, and an image that represents the gray-level model associated to the segmentation. Then, assuming a conditional ordering relation, we show that any region merging/classification segmentation algorithm can be defined as an extensive operator in such a complete product lattice, in the second place, we proposed a very similar lattice-based extended representation of gray-level functions in the context of connected operators, that highlights the mathematical analogy with segmentation algorithms, but in which the ordering relation is different. We use this framework to show that every region merging/classification segmentation algorithm indeed corresponds to a connected operator. While this result provides an explanation to previous work in the area, it also opens possibilities for further analysis in the two domains. From this perspective, we additionally study some theoretical properties of a general region merging segmentation algorithm.

Looking at Cities in Mexico with Crowds

Mobile and social technologies are providing new opportunities to document, characterize, and gather impressions of urban environments. In this paper, we present a study that examines urban perceptions of three cities in central Mexico (Guanajuato, Leon and Silao), which integrates a mobile crowdsourcing framework to collect geo-localized images of urban environments by a local youth community, and an online crowdsourcing platform (Amazon Mechanical Turk) to gather impressions of urban environments along twelve physical and psychological dimensions. Our study resulted in a collection of 7,000 geo-localized images containing outdoor scenes and views of each citys built environment, including touristic, historical, and residential neighbourhoods; and 156,000 individual judgments from MTurk. Statistical analyses show that outdoor environments can be reliably assessed with respect to most urban dimensions by the observers of crowdsourced images. Furthermore, a cross-city statistical analysis shows that outdoor urban places in Guanajuato (a touristic, cultural heritage site) are perceived as more quiet, picturesque and interesting compared to places in Leon and Silao, which are commercial and industrial hubs, respectively. In contrast Silao, is perceived to have lower accessibility than Leon. Finally, we investigate whether the perceptions of urban environments vary across different times of the day and found that places in the evening are perceived as less happy, pleasant and preserved, when compared to the same place in the morning. Through the use of collective action, participatory sensing and mobile crowdsourcing, our study engages citizens to understand socio-urban problems in their communities.

Happy and agreeable?: multi-label classification of impressions in social video

The mobile and ubiquitous nature of conversational social video has placed video blogs among the most popular forms of online video. For this reason, there has been an increasing interest in conducting studies of human behavior from video blogs in affective and social computing. In this context, we consider the problem of mood and personality trait impression inference using verbal and nonverbal audio-visual features. Under a multi-label classification framework, we show that for both mood and personality trait binary label sets, not only the simultaneous inference of multiple labels is feasible, but also that classification accuracy increases moderately for several labels, compared to a single-label approach. The multi-label method we consider naturally exploits label correlations, which motivate our approach, and our results are consistent with models proposed in psychology to define human emotional states and personality. Our approach points to the automatic specification of co-occurring emotional states and personality, by inferring several labels at once, compared to single-label approaches. We also propose a new set of facial features, based on emotion valence from facial expressions, and analyze their suitability in the multi-label framework.

Efficient Reconstruction of Complex 3-D Scenes from Incomplete RGB-D Data

In this paper we develop a new approach for reconstructing 3-D scenes from RGB-D data. We use a Markov random field to model appearance relations and geometric cues between different regions of a scene, as a means to provide robustness to noisy and incomplete data often generated by RGB-D devices. A parametric reconstruction of 3-D scenes that enable coherent physical interaction are computed, in near real time, with a standard computer that does not use specialized hardware.

Check Out This Place: Inferring Ambiance From Airbnb Photos

Airbnb is changing the landscape of the hospitality industry, and to this day, little is known about the inferences that guests make about Airbnb listings. Our work constitutes a first attempt at understanding how potential Airbnb guests form first impressions from images, one of the main modalities featured on the platform. We contribute to the multimedia community by proposing the novel task of automatically predicting human impressions of ambiance from pictures of listings on Airbnb. We collected Airbnb images, focusing on the countries Switzerland and Mexico as case studies, and used crowdsourcing mechanisms to gather annotations on physical and ambiance attributes, finding that agreement among raters was high for most of the attributes. Our cluster analysis showed that both physical and psychological attributes could be grouped into three clusters. We then extracted state-of-the-art features from the images to automatically infer the annotated variables in a regression task. Results show the feasibility of predicting ambiance impressions of homes on Airbnb, with up to 42% of the variance explained by our model, and best results were obtained using activation layers of deep convolutional neural networks trained on the Places dataset, a collection of scene-centric images.

Insiders and Outsiders: Comparing Urban Impressions between Population Groups

There is a growing interest in social and urban computing to employ crowdsourcing as means to gather impressions of urban perception for indoor and outdoor environments. Previous studies have established that reliable estimates of urban perception can be obtained using online crowdsourcing systems, but implicitly assumed that the judgments provided by the crowd are not dependent on the background knowledge of the observer. In this paper, we investigate how the impressions of outdoor urban spaces judged by online crowd annotators, compare with the impressions elicited by the local inhabitants, along six physical and psychological labels. We focus our study in a developing city where understanding and characterization of these socio-urban perceptions is of societal importance. We found statistically significant differences between the two population groups. Locals perceived places to be more dangerous and dirty, when compared with online crowd workers; while online annotators judged places to be more interesting in comparison to locals. Our results highlight the importance of the degree of familiarity with urban spaces and background knowledge while rating urban perceptions, which is lacking in some of the existing work in urban computing.