Mutimodal Ranking Optimization for Heterogeneous Face Re-identification

Heterogeneous face re-identification, namely matching heterogeneous faces across disjoint visible light (VIS) and near-infrared (NIR) cameras, has become an important problem in video surveillance application. However, the large domain discrepancy between heterogeneous NIR-VIS faces makes the performance of face re-identification degraded dramatically. To solve this problem, a multimodal fusion ranking optimization algorithm for heterogeneous face re-identification is proposed in this paper. Firstly, we design a heterogeneous face translation network to obtain multimodal face pairs, including NIR-VIS/NIR-NIR/VIS-VIS face pairs, through mutual transformation between NIR-VIS faces. Secondly, we propose linear and non-linear fusion strategies to aggregate initial ranking lists of multimodal face pairs and acquire the optimized re-ranked list based on modal complementarity. The experimental results show that the proposed multimodal fusion ranking optimization algorithm can effectively utilize the complementarity and outperforms some relative methods on the SCface dataset

The Effectiveness of Concept Based Search for Video Retrieval

In this paper we investigate how a small number of high-level concepts\ud derived for video shots, such as Sport. Face.Indoor. etc., can be used effectively for ad hoc search in video material. We will answer the following questions: 1) Can we automatically construct concept queries from ordinary text queries? 2) What is the best way to combine evidence from single concept detectors into final search results? We evaluated algorithms for automatic concept query formulation using WordNet based concept extraction, and we evaluated algorithms for fast, on-line combination of concepts. Experimental results on data from the TREC Video 2005 workshop and 25 test users show the following. 1) Automatic query formulation through WordNet based concept extraction can achieve comparable results to user created query concepts and 2) Combination methods that take neighboring shots into account outperform more simple combination methods

Large-scale Fully-Unsupervised Re-Identification

Fully-unsupervised Person and Vehicle Re-Identification have received increasing attention due to their broad applicability in surveillance, forensics, event understanding, and smart cities, without requiring any manual annotation. However, most of the prior art has been evaluated in datasets that have just a couple thousand samples. Such small-data setups often allow the use of costly techniques in time and memory footprints, such as Re-Ranking, to improve clustering results. Moreover, some previous work even pre-selects the best clustering hyper-parameters for each dataset, which is unrealistic in a large-scale fully-unsupervised scenario. In this context, this work tackles a more realistic scenario and proposes two strategies to learn from large-scale unlabeled data. The first strategy performs a local neighborhood sampling to reduce the dataset size in each iteration without violating neighborhood relationships. A second strategy leverages a novel Re-Ranking technique, which has a lower time upper bound complexity and reduces the memory complexity from O(n^2) to O(kn) with k << n. To avoid the pre-selection of specific hyper-parameter values for the clustering algorithm, we also present a novel scheduling algorithm that adjusts the density parameter during training, to leverage the diversity of samples and keep the learning robust to noisy labeling. Finally, due to the complementary knowledge learned by different models, we also introduce a co-training strategy that relies upon the permutation of predicted pseudo-labels, among the backbones, with no need for any hyper-parameters or weighting optimization. The proposed methodology outperforms the state-of-the-art methods in well-known benchmarks and in the challenging large-scale Veri-Wild dataset, with a faster and memory-efficient Re-Ranking strategy, and a large-scale, noisy-robust, and ensemble-based learning approach.Comment: This paper has been submitted for possible publication in an IEEE Transaction

Re-ranking person re-identification with k-reciprocal encoding

© 2017 IEEE. When considering person re-identification (re-ID) as a retrieval process, re-ranking is a critical step to improve its accuracy. Yet in the re-ID community, limited effort has been devoted to re-ranking, especially those fully automatic, unsupervised solutions. In this paper, we propose a k-reciprocal encoding method to re-rank the re-ID results. Our hypothesis is that if a gallery image is similar to the probe in the k-reciprocal nearest neighbors, it is more likely to be a true match. Specifically, given an image, a k- reciprocal feature is calculated by encoding its k-reciprocal nearest neighbors into a single vector, which is used for reranking under the Jaccard distance. The final distance is computed as the combination of the original distance and the Jaccard distance. Our re-ranking method does not require any human interaction or any labeled data, so it is applicable to large-scale datasets. Experiments on the largescale Market-1501, CUHK03, MARS, and PRW datasets confirm the effectiveness of our method1

Neighborhood Crime and Travel Behavior: An Investigation of the Influence of Neighborhood Crime Rates on Mode Choice – Phase II, MTI Report 11-04

There are considerable environmental and public health benefits if people choose to walk, bicycle, or ride transit, instead of drive. However, little work has been done on the effects of neighborhood crimes on mode choice. Instinctively, we understand that the threats posed by possible criminal activity in one’s neighborhood can play a major role in the decision to drive, take transit, walk or ride a bicycle, but so far little empirical evidence supports this notion, let alone guides public infrastructure investments, land use planning, or the allocation of police services. This report--describing Phase 2 of a research study conducted for the Mineta Transportation Institute on crime and travel behavior – finds that high crime neighborhoods tend to discourage residents from walking or riding a bicycle. When comparing a high crime to a lower crime neighborhood the odds of walking over choosing auto decrease by 17.25 percent for work trips and 61 percent for non-work trips. For transit access to work trips, the odds of choosing walk/bike to a transit station over auto decrease by 48.1 percent. Transit trips, on the other hand, are affected by neighborhood crime levels in a similar way to auto trips, wherein high crime neighborhoods appear to encourage transit mode choice. The odds of taking transit over choosing auto increase by 17.25 percent for work trips and 164 percent for non-work trips. Surprised by this last finding, the research team tested two possible explanations for why high levels of neighborhood crime would increase transit use: 1) the mode choice models do not adequately account for the effects and interplay between urban form and crime levels and mode choice; and 2) people who ride in cars or take transit may feel more protected when riding in a vehicle (termed here, the “neighborhood exposure hypothesis”). To investigate the first explanation, the researchers tested a number of alternative urban form and crime interaction variables to no effect. Digging deeper into the second hypothesis, the researchers tested whether the access portion of transit trips (walking, bicycling, or driving to a transit stop) is sensitive to neighborhood crimes as well, wherein high crime neighborhoods discourage walking and bicycling and encourage driving to transit stations. The report provides evidence that high crime neighborhoods encourage driving to transit stops and discourage walking or bicycling, lending support to the neighborhood exposure hypothesis

Geospatial Analysis of Smart Growth and Transit Oriented Developments

Rapid population growth, water and air pollution, overloaded public services, and traffic congestion can stress economies and result in unsustainable communities. Geographic Information Science (GIS) is a valuable technology for reversing negative these types of trends by identifying areas that may presently exhibit Smart Growth characteristics. Rather than expand on the current transportation infrastructure and develop new Smart Growth communities, San Diego Metropolitan’s Transit System (SDMTS) intends to use this study to review the current transit routing infrastructure in an effort to support communities exhibiting Smart Growth potential. Communities along existing transportation infrastructure will analyzed as potential Smart Growth and TOD communities. After the study is completed, SDMTS will have the geospatial data necessary for supporting future Smart Growth development

SHIRAZ: an automated histology image annotation system for zebrafish phenomics

Histological characterization is used in clinical and research contexts as a highly sensitive method for detecting the morphological features of disease and abnormal gene function. Histology has recently been accepted as a phenotyping method for the forthcoming Zebrafish Phenome Project, a large-scale community effort to characterize the morphological, physiological, and behavioral phenotypes resulting from the mutations in all known genes in the zebrafish genome. In support of this project, we present a novel content-based image retrieval system for the automated annotation of images containing histological abnormalities in the developing eye of the larval zebrafish