Roger Zimmermann

Fusion of Multichannel Local and Global Structural Cues for Photo Aesthetics Evaluation

Photo aesthetic quality evaluation is a fundamental yet under addressed task in computer vision and image processing fields. Conventional approaches are frustrated by the following two drawbacks. First, both the local and global spatial arrangements of image regions play an important role in photo aesthetics. However, existing rules, e.g., visual balance, heuristically define which spatial distribution among the salient regions of a photo is aesthetically pleasing. Second, it is difficult to adjust visual cues from multiple channels automatically in photo aesthetics assessment. To solve these problems, we propose a new photo aesthetics evaluation framework, focusing on learning the image descriptors that characterize local and global structural aesthetics from multiple visual channels. In particular, to describe the spatial structure of the image local regions, we construct graphlets small-sized connected graphs by connecting spatially adjacent atomic regions. Since spatially adjacent graphlets distribute closely in their feature space, we project them onto a manifold and subsequently propose an embedding algorithm. The embedding algorithm encodes the photo global spatial layout into graphlets. Simultaneously, the importance of graphlets from multiple visual channels are dynamically adjusted. Finally, these post-embedding graphlets are integrated for photo aesthetics evaluation using a probabilistic model. Experimental results show that: 1) the visualized graphlets explicitly capture the aesthetically arranged atomic regions; 2) the proposed approach generalizes and improves four prominent aesthetic rules; and 3) our approach significantly outperforms state-of-the-art algorithms in photo aesthetics prediction.

Peer-to-Peer Media Streaming: Insights and New Developments

Internet media content delivery started to emerge roughly a decade ago, and it has subsequently had a major impact on network traffic and usage. Although traditional client-server systems were used initially for delivering media content, researchers and practitioners soon realized that peer-to-peer (P2P) systems, due to their self-scaling properties, had the potential to improve scalability compared with traditional client-server architectures. Consequently, various P2P media streaming systems have been deployed successfully, and corresponding theoretical investigations have been performed on such systems. The rapid developments in this field raise the need for up-to-date literature surveys to summarize them. In recent years, numerous technological discoveries have been achieved. The focus of this report is to survey and discuss these new findings, which include new technological developments, as well as new understandings of these developments and of the existing P2P streaming techniques, through both novel modeling methodologies and measurement-based studies.

Viewable scene modeling for geospatial video search

Video sensors are becoming ubiquitous and the volume of captured video material is very large. Therefore, tools for searching video databases are indispensable. Current techniques that extract features purely based on the visual signals of a video are struggling to achieve good results. By considering video related meta-information, more relevant and precisely delimited search results can be obtained. In this study we propose a novel approach for querying videos based on the notion that the geographical location of the captured scene in addition to the location of a camera can provide valuable information and may be used as a search criterion in many applications. This study provides an estimation model of the viewable area of a scene for indexing and searching and reports on a prototype implementation. Among our objectives is to stimulate a discussion of these topics in the research community as information fusion of different georeferenced data sources is becoming increasingly important. Initial results illustrate the feasibility of the proposed approach.

SCADDAR: an efficient randomized technique to reorganize continuous media blocks

Scalable storage architectures allow for the addition of disks to increase storage capacity and/or bandwidth. In its general form, disk scaling also refers to disk removals when either capacity needs to be conserved or old disk drives are retired. Assuming random placement of blocks on multiple nodes of a continuous media server, our optimization objective is to redistribute a minimum number of media blocks after disk scaling. This objective should be met under two restrictions. First, uniform distribution and hence a balanced load should be ensured after redistribution. Second, the redistributed blocks should be retrieved at the normal mode of operation in one disk access and through low complexity computation. We propose a technique that meets the objective, while we prove that it also satisfies both restrictions. The SCADDAR approach is based on using a series of REMAP functions which can derive the location of a new block using only its original location as a basis.

Yima: a second-generation continuous media server

Continuous media data requires a streaming architecture that can manage real-time delivery constraints and address the large size of CM objects. Although commercial systems ordinarily use proprietary technology and algorithms, making it difficult to compare their products with research prototypes, the authors have designed and developed Yima, a second-generation CM server that demonstrates several advanced concepts. Although this system has not yet achieved the refinement of commercial solutions, it is operational and incorporates lessons learned from first-generation research prototypes, including complete distribution, efficient online scalability, and synchronization of several media streams within a single frame. The paper discusses the multinode server architecture.

From remote media immersion to Distributed Immersive Performance

We present the architecture, technology and experimental applications of a real-time, multi-site, interactive and collaborative environment called Distributed Immersive Performance (DIP). The objective of DIP is to develop the technology for live, interactive musical performances in which the participants - subsets of musicians, the conductor and the audience - are in different physical locations and are interconnected by very high fidelity multichannel audio and video links. DIP is a specific realization of broader immersive technology - the creation of the complete aural and visual ambience that places a person or a group of people in a virtual space where they can experience events occurring at a remote site or communicate naturally regardless of their location. The DIP experimental system has interaction sites and servers in different locations on the USC campus and at several partners, including the New World Symphony of Miami Beach, FL. The sites have different types of equipment to test the effects of video and audio fidelity on the ease of use and functionality for different applications. Many sites have high-definition (HD) video or digital video (DV) quality images projected onto wide screen wall displays completely integrated with an immersive audio reproduction system for a seamless, fully three-dimensional aural environment with the correct spatial sound localization for participants. The system is capable of storage and playback of the many streams of synchronized audio and video data (immersidata), and utilizes novel protocols for the low-latency, seamless, synchronized real-time delivery of immersidata over local area networks and wide-area networks such as Internet2. We discuss several recent interactive experiments using the system and many technical challenges common to the DIP scenario and a broader range of applications. These challenges include: (1). low latency continuous media (CM) stream transmission, synchronization and data loss management; (2). low latency, real-time video and multichannel immersive audio acquisition and rendering; (3). real-time continuous media stream recording, storage, playback; (4). human factors studies: psychophysical, perceptual, artistic, performance evaluation; (5). robust integration of all these technical areas into a seamless presentation to the participants.

Mitra: A Scalable Continuous Media Server

Mitra is a scalable storage manager that supports the display of continuous media data types, e.g., audio and video clips. It is a software based system that employs off-the-shelf hardware components. Its present hardware platform is a cluster of multi-disk workstations, connected using an ATM switch. Mitra supports the display of a mix of media types. To reduce the cost of storage, it supports a hierarchical organization of storage devices and stages the frequently accessed objects on the magnetic disks. For the number of displays to scale as a function of additional disks, Mitra employs staggered striping. It implements three strategies to maximize the number of simultaneous displays supported by each disk. First, the EVEREST file system allows different files (corresponding to objects of different media types) to be retrieved at different block size granularities. Second, the FIXB algorithm recognizes the different zones of a disk and guarantees a continuous display while harnessing the average disk transfer rate. Third, Mitra implements the Grouped Sweeping Scheme (GSS) to minimize the impact of disk seeks on the available disk bandwidth.In addition to reporting on implementation details of Mitra, we present performance results that demonstrate the scalability characteristics of the system. We compare the obtained results with theoretical expectations based on the bandwidth of participating disks. Mitra attains between 65% to 100% of the theoretical expectations.

An energy-efficient voting-based clustering algorithm for sensor networks

Clustering provides an effective way for extending the lifetime of a sensor network. In this paper, we propose a novel voting-based clustering algorithm (VCA) for energy-efficient data dissemination in quasi-stationary sensor networks. This new approach lets sensors vote for their neighbors to elect suitable cluster heads. Unlike traditional distributed clustering algorithms, VCA combines load balancing, energy and topology information together by using very simple voting mechanisms. This new approach is completely distributed, location-unaware and independent of network size and topology. Simulation results show that our VCA can reduce the number of clusters by 5-25% and prolong the lifetime of a sensor network by 25-30% over that of existing energy-efficient clustering protocols.

The multi-rule partial sequenced route query

Trip planning search (TPS) represents an important class of queries in Geographic Information Systems (GIS). In many real-world applications, TPS requests are issued with a number of constraints. Unfortunately, most of these constrained TPS cannot be directly answered by any of the existing algorithms. By formulating each restriction into rules, we propose a novel form of route query, namely the multi-rule partial sequenced route (MRPSR) query. Our work provides a unified framework that also subsumes the well-known trip planning query (TPQ) and the optimal sequenced route (OSR) query. In this paper, we first prove that MRPSR is NP-hard and then present three heuristic algorithms to search for near-optimal solutions for the MRPSR query. Our extensive simulations show that all of the proposed algorithms can answer the MRPSR query effectively and efficiently. Using both real and synthetic datasets, we investigate the performance of our algorithms with the metrics of the route distance and the response time in terms of the percentage of the constrained points of interest (POI) categories. Compared to the LORD-based brute-force solution, the response times of our algorithms are remarkably reduced while the resulting route length is only slightly longer than the shortest route.

Location-Based Spatial Query Processing with Data Sharing in Wireless Broadcast Environments

Location-based spatial queries (LBSQs) refer to spatial queries whose answers rely on the location of the inquirer. Efficient processing of LBSQs is of critical importance with the ever-increasing deployment and use of mobile technologies. We show that LBSQs have certain unique characteristics that traditional spatial query processing in centralized databases does not address. For example, a significant challenge is presented by wireless broadcasting environments, which have excellent scalability but often exhibit high-latency database access. In this paper, we present a novel query processing technique that, while maintaining high scalability and accuracy, manages to reduce the latency considerably in answering location-based spatial queries. Our approach is based on peer-topeer sharing, which enables us to process queries without delay at a mobile host by using query results cached in its neighboring mobile peers. We demonstrate the feasibility of our approach through a probabilistic analysis, and we illustrate the appeal of our technique through extensive simulation results.