Person re-identification via efficient inference in fully connected CRF

In this paper, we address the problem of person re-identification problem, i.e., retrieving instances from gallery which are generated by the same person as the given probe image. This is very challenging because the person's appearance usually undergoes significant variations due to changes in illumination, camera angle and view, background clutter, and occlusion over the camera network. In this paper, we assume that the matched gallery images should not only be similar to the probe, but also be similar to each other, under suitable metric. We express this assumption with a fully connected CRF model in which each node corresponds to a gallery and every pair of nodes are connected by an edge. A label variable is associated with each node to indicate whether the corresponding image is from target person. We define unary potential for each node using existing feature calculation and matching techniques, which reflect the similarity between probe and gallery image, and define pairwise potential for each edge in terms of a weighed combination of Gaussian kernels, which encode appearance similarity between pair of gallery images. The specific form of pairwise potential allows us to exploit an efficient inference algorithm to calculate the marginal distribution of each label variable for this dense connected CRF. We show the superiority of our method by applying it to public datasets and comparing with the state of the art.Comment: 7 pages, 4 figure

Effective Instance Matching for Heterogeneous Structured Data

One main problem towards the effective usage of structured data is instance matching, where the goal is to find instance representations referring to the same real-world thing. In this book we investigate how to effectively match Heterogeneous structured data. We evaluate our approaches against the latest baselines. The results show advances beyond the state-of-the-art

Towards Scalable Real-Time Entity Resolution using a Similarity-Aware Inverted Index Approach

Most research into entity resolution (also known as record linkage or data matching) has concentrated on the quality of the matching results. In this paper, we focus on matching time and scalability, with the aim to achieve large-scale real-time entity resolution. Traditional entity resolution techniques have as-sumed the matching of two static databases. In our networked and online world, however, it is becoming increasingly important for many organisations to be able to conduct entity resolution between a collection of often very large databases and a stream of query or update records. The matching should be done in (near) real-time, and be as automatic and accurate as possible, returning a ranked list of matched records for each given query record. This task therefore be-comes similar to querying large document collections, as done for example by Web search engines, however based on a different type of documents: structured database records that, for example, contain personal information, such as names and addresses. In this paper, we investigate inverted indexing techniques, as commonly used in Web search engines, and employ them for real-time entity resolution. We present two variations of the traditional inverted in-dex approach, aimed at facilitating fast approximate matching. We show encouraging initial results on large real-world data sets, with the inverted index ap-proaches being up-to one hundred times faster than the traditionally used standard blocking approach. However, this improved matching speed currently comes at a cost, in that matching quality for larger data sets can be lower compared to when tandard blocking is used, and thus more work is required

Fuzzy logic-based approximate event notification in sparse MANETs

Mobile Ad-Hoc Networks (MANETs) are an important communication infrastructure to support emergency and rescue operations. To address the frequent disconnections and network partitions that might occur, we have developed a distributed event notification service (DENS) for sparse MANETs. In most event notification solutions, subscriptions are formed with crisp values or crisp value ranges. However, in emergency and rescue operations subscribers may not always have time to give crisp values or crisp value ranges. Moreover, subscriber's interests in queries have gradual nature and subjective measure that calls for computing by words. Therefore, we design and implement a simple fuzzy concept based subscription language allowing more expressive subscriptions and more sophisticated event-filtering. It is built on two new ideas: using features as multi-attribute indexes of the subscription and predicate patterns for processing subscriptions with arbitrary Boolean operators. However, requiring more computational efforts, fuzzy logic introduces performance penalties in the whole network. The proposed services have been evaluated for run-time, space and scalability efficiency. The proposed design framework is extensible to the user- and application-semantics and configurable to the dynamics in data that publish/subscribe paradigm imposes at runtime