Multiple Instance Learning: A Survey of Problem Characteristics and Applications

Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research

Semantic Part Segmentation using Compositional Model combining Shape and Appearance

In this paper, we study the problem of semantic part segmentation for animals. This is more challenging than standard object detection, object segmentation and pose estimation tasks because semantic parts of animals often have similar appearance and highly varying shapes. To tackle these challenges, we build a mixture of compositional models to represent the object boundary and the boundaries of semantic parts. And we incorporate edge, appearance, and semantic part cues into the compositional model. Given part-level segmentation annotation, we develop a novel algorithm to learn a mixture of compositional models under various poses and viewpoints for certain animal classes. Furthermore, a linear complexity algorithm is offered for efficient inference of the compositional model using dynamic programming. We evaluate our method for horse and cow using a newly annotated dataset on Pascal VOC 2010 which has pixelwise part labels. Experimental results demonstrate the effectiveness of our method

Conservation priorities for Prunus africana defined with the aid of spatial analysis of genetic data and climatic variables

Conservation priorities for Prunus africana, a tree species found across Afromontane regions, which is of great commercial interest internationally and of local value for rural communities, were defined with the aid of spatial analyses applied to a set of georeferenced molecular marker data (chloroplast and nuclear microsatellites) from 32 populations in 9 African countries. Two approaches for the selection of priority populations for conservation were used differing in the way they optimize representation of intra-specific diversity of P. africana across a minimum number of populations. The first method (Si) was aimed at maximizing genetic diversity of the conservation units and their distinctiveness with regard to climatic conditions, the second method (S2) at optimizing representativeness of the genetic diversity found throughout the species' range. Populations in East African countries (especially Kenya and Tanzania) were found to be of great conservation value, as suggested by previous findings. These populations are complemented by those in Madagascar and Cameroon. The combination of the two methods for prioritization led to the identification of a set of 6 priority populations. The potential distribution of P. africana was then modeled based on a dataset of 1,500 georeferenced observations. This enabled an assessment of whether the priority populations identified are exposed to threats from agricultural expansion and climate change, and whether they are located within the boundaries of protected areas. The range of the species has been affected by past climate change and the modeled distribution of P. africana indicates that the species is likely to be negatively affected in future, with an expected decrease in distribution by 2050. Based on these insights, further research at the regional and national scale is recommended, in order to strengthen P. africana conservation efforts

Developing GIS-based eastern equine encephalitis vector-host models in Tuskegee, Alabama

<p>Abstract</p> <p>Background</p> <p>A site near Tuskegee, Alabama was examined for vector-host activities of eastern equine encephalomyelitis virus (EEEV). Land cover maps of the study site were created in ArcInfo 9.2^®from QuickBird data encompassing visible and near-infrared (NIR) band information (0.45 to 0.72 μm) acquired July 15, 2008. Georeferenced mosquito and bird sampling sites, and their associated land cover attributes from the study site, were overlaid onto the satellite data. SAS 9.1.4^®was used to explore univariate statistics and to generate regression models using the field and remote-sampled mosquito and bird data. Regression models indicated that <it>Culex erracticus </it>and Northern Cardinals were the most abundant mosquito and bird species, respectively. Spatial linear prediction models were then generated in Geostatistical Analyst Extension of ArcGIS 9.2^®. Additionally, a model of the study site was generated, based on a Digital Elevation Model (DEM), using ArcScene extension of ArcGIS 9.2^®.</p> <p>Results</p> <p>For total mosquito count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 5.041 km, nugget of 6.325 km, lag size of 7.076 km, and range of 31.43 km, using 12 lags. For total adult <it>Cx. erracticus </it>count, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 5.764 km, nugget of 6.114 km, lag size of 7.472 km, and range of 32.62 km, using 12 lags. For the total bird count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 4.998 km, nugget of 5.413 km, lag size of 7.549 km and range of 35.27 km, using 12 lags. For the Northern Cardinal count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 6.387 km, nugget of 5.935 km, lag size of 8.549 km and a range of 41.38 km, using 12 lags. Results of the DEM analyses indicated a statistically significant inverse linear relationship between total sampled mosquito data and elevation (R²= -.4262; p < .0001), with a standard deviation (SD) of 10.46, and total sampled bird data and elevation (R²= -.5111; p < .0001), with a SD of 22.97. DEM statistics also indicated a significant inverse linear relationship between total sampled <it>Cx. erracticus </it>data and elevation (R²= -.4711; p < .0001), with a SD of 11.16, and the total sampled Northern Cardinal data and elevation (R²= -.5831; p < .0001), SD of 11.42.</p> <p>Conclusion</p> <p>These data demonstrate that GIS/remote sensing models and spatial statistics can capture space-varying functional relationships between field-sampled mosquito and bird parameters for determining risk for EEEV transmission.</p

Disruption and disease: How does population management affect disease risk in wild bird populations?

Despite the ubiquity of wildlife management, from reintroductions and supplemental feeding to culling and habitat destruction, very little is known of the effects of management practices on species’ social dynamics. Species’ social structure has the potential to affect not only behaviour and evolution but also the transmission of information or disease. Understanding the effects of population management on social behaviour and organisation is a key step in understanding these species’ ecology. This thesis examines the differences between individuals’ roles in the social structure and what this means for the transmission of disease. It demonstrates how similarity in movement behaviour scales with increasing social circles, how seasonality in movement and seasonality in association rates covary as well as detailing post-cull behavioural changes. It finds that there is the potential for certain individuals (most likely non-breeding individuals) to transmit infection far and wide. It reveals the similarities in movement behaviour and body condition that birds share with their pair and social group. It emphasises the importance of autumn and winter movement in the transmission of infectious disease and it follows the short- and long-term changes in social structure and movement behaviour following a cull. Cull survivors were observed to retain a higher proportion of associations with their previous associates and moved less far in the year following the cull than in the year preceding it. This is the first application of social network analysis to quantify social structure before and after culling. The findings suggest that culling an infected population may facilitate rather than constrain the transmission of disease

Understanding social relationships in egocentric vision

The understanding of mutual people interaction is a key component for recognizing people social behavior, but it strongly relies on a personal point of view resulting difficult to be a-priori modeled. We propose the adoption of the unique head mounted cameras first person perspective (ego-vision) to promptly detect people interaction in different social contexts. The proposal relies on a complete and reliable system that extracts people\u5f3s head pose combining landmarks and shape descriptors in a temporal smoothed HMM framework. Finally, interactions are detected through supervised clustering on mutual head orientation and people distances exploiting a structural learning framework that specifically adjusts the clustering measure according to a peculiar scenario. Our solution provides the flexibility to capture the interactions disregarding the number of individuals involved and their level of acquaintance in context with a variable degree of social involvement. The proposed system shows competitive performances on both publicly available ego-vision datasets and ad hoc benchmarks built with real life situations

Towards solving computer vision problems: datasets, labels, algorithms, and applications

The solution to a supervised computer vision problem consists of an application, algorithm, input data, and a set of human generated labels. Solving these kinds of tasks involves collecting large quantities of data, collecting appropriate labels, and developing machine vision algorithms tailored to the application. Progress on these problems has often benefited from large scale datasets with high fidelity labels. Successful algorithms display a synergy between application goals and the size and quality of the dataset. This thesis presents work highlighting the importance of each component of a supervised vision task.First, the problem of automatically classifying groups of people into social categories is introduced. This problem is called Urban Tribe Classification. To tackle this problem, each individual and the entire group of individuals are modeled. Since this was a newly introduced computer vision problem, a dataset for this task was created. On this dataset, the combined representation of group and individuals outperforms using only the person representations. This model showed promising results for automatic subculture classification.Second, the problem of creating perceptual embeddings based on human similarity judgements is tackled. This work focuses on triplet similarity comparisons of the form ``Is object $i$ more similar to $j$ or $k$?'', which have been useful for computer vision and machine learning applications. Unfortunately, triplet similarity comparisons, like many human labeling efforts, can be prohibitively expensive. This work proposes two techniques for dealing with this obstacle. First, an alternative display for collecting triplets is designed. This display shows a probe image and a grid of query images, allowing the user to collect multiple triplets simultaneously. The display is shown to reduce the cost and time of triplet collection. In addition, higher quality embeddings are created with the improved triplet collection UI. A 10,000-food item dataset of human taste similarity was created using this UI. Second, ``SNaCK,'' a low-dimensional perceptual embedding algorithm that combines human expertise with automatic machine kernels, is introduced. Both parts are complementary: human insight can capture relationships that are not apparent from the object's visual similarity and the machine can help relieve the human from having to exhaustively specify many constraints. Finally, the precise localization of key frames of an action is explored. This work focuses on detecting the exact starting frame of a behavior, an important task for neuroscience research. To address this problem, a loss designed to penalize extra and missed action start detections over small misalignments. Recurrent neural networks (RNN) are trained to optimize this loss. The model is shown to reduce the number of false positives, an important criteria defined by the neuroscientist. The performance of the model is evaluated on a new dataset, the Mouse Reach Dataset, a large, annotated video dataset of mice performing a sequence of actions. The dataset was created for neuroscience research. On this dataset, the proposed model outperforms related approaches and baseline methods using an unstructured loss