6035916
Ranking web objects from multiple communities
Vertical search is a promising direction as it leverages domain-specific knowledge and can provide more precise information for users. In this paper, we study the Web object-ranking problem, one of the key issues in building a vertical search engine. More specifically, we focus on this problem in cases when objects lack relationships between different Web communities, and take high-quality photo search as the test bed for this investigation. We proposed two score fusion methods that can automatically integrate as many Web communities (Web forums) with rating information as possible. The proposed fusion methods leverage the hidden links discovered by a duplicate photo detection algorithm, and aims at minimizing score differences of duplicate photos in different forums. Both intermediate results and user studies show the proposed fusion methods are practical and efficient solutions to Web object ranking in cases we have described. Though the experiments were conducted on high-quality photo ranking, the proposed algorithms are also applicable to other ranking problems, such as movie ranking and music ranking.
32640232
AP-Based Borda Voting Method for Feature Extraction in TRECVID-2004
We present a novel fusion method — AP-based Borda voting method (APBB)— for rankings. Due to its adaptive weighting scheme, APBB outperforms many traditional methods. Comparative experiments on TRECVID 2004 data were carried out and showed the robustness and effectiveness of this method.
18118919
EnjoyPhoto: a vertical image search engine for enjoying high-quality photos
In this paper, we propose building a vertical image search engine called EnjoyPhoto that leverages rich metadata from various photo forum web sites to meet users' requirements for enjoying high-quality photos, which is virtually impossible in traditional image search engines. To solve the ranking problem when aggregating multiple photo forums, we propose a novel rank fusion algorithm that uses duplicate photos to normalize rating scores. To further improve user experiences in enjoying photos, we design an in-place image browsing interface, and compare it with several other interfaces in a user study. With rich metadata and rating information, more attractive user interfaces are enabled, including slideshow authoring and photo recommendations. We conducted experiments and user studies on a 2.5-million image database to evaluate the proposed rank fusion algorithm, investigate the rationale behind building a vertical image search engine, and study user interfaces and preferences for the purpose of enjoying high-quality photos. The experimental results demonstrate the effectiveness of the proposed ranking algorithm. The results also show that the 2.5-million high-quality image database in EnjoyPhoto performs comparably with Google's 1- billion image database for queries related to location, nature, and daily life categories. Finally, our results show that the in-place browsing interface-called Force-Transfer view-is much more convenient for users than traditional interfaces.
10396270
Temporal Shot Clustering Analysis for Video Concept Detection
The phenomenon that conceptually related shots appear together in videos is called temporal shot clustering. This phenomenon is a useful cue for video concept detection, which is one of basic steps in content-based video indexing and retrieval. We propose a method, called temporal shot clustering analysis, to improve results of video concept detection by exploiting the temporal shot clustering phenomenon. Two other methods are compared with temporal shot clustering analysis on the TRECVID 2003 dataset. Experiments showed that temporal shot clustering is of much benefit for video concept detection, and that temporal shot clustering method outperforms the other methods.
4777365
Argos: Efficient Refresh in an XQL-Based Web Caching System
The Web has become a major conduit to information repositories of all kinds. Web caches are employed to store web views to provide an immediate response to recurring queries. However, the accuracy of the replicates in web caches encounters challenges due to the dynamicity of web data. We are thus developing and evaluating a web caching system equipped with an efficient refresh strategy. With the assistance of a novel index structure - the Aggregation Path Index (APIX), we built Argos, a web caching system based on the GMD XQL query engine. Argos achieves a high degree of self-maintenance by diagnosing irrelevant data update cases. It hence greatly improves the refresh performance of the materialized web view. We also report preliminary experimental results assessing the performance of Argos compared to the state-of-the-art solution in the literature.
41515158
Error resilience for block loss with overlapped motion compensation
A new error resilience algorithm for recovering the lost blocks or motion vectors is presented. It combines the overlapped motion compensation and the side match criterion to make the effect of lost blocks subjectively imperceptible. The side match criterion takes advantage of the spatial contiguity and interpixel correlation of image to select the best-fit replacement among the motion vectors of spatially contiguous candidate blocks. Particularly, to mask the blocking artifacts, we incorporate an overlapping technique to create a subjectively closer approximation to the true error-free image.
2603707
Evaluating Critiquing-based Recommender Agents
We describe a user study evaluating two critiquing-based recommender agents based on three criteria: decision accuracy. decision effort, and user confidence. Results show that user-motivated critiques were more frequently applied and the example critiquing system employing only this type of critiques achieved the best results. In particular, the example critiquing agent significantly improves users' decision accuracy with less cognitive effort consumed than the dynamic critiquing recommender with system-proposed critiques. Additionally, the former is more likely to inspire users' confidence of their choice and promote their intention to purchase and return to the agent for future use.
5457020
Identifying metabolic enzymes with multiple types of association evidence
BackgroundExisting large-scale metabolic models of sequenced organisms commonly include enzymatic functions which can not be attributed to any gene in that organism. Existing computational strategies for identifying such missing genes rely primarily on sequence homology to known enzyme-encoding genes.ResultsWe present a novel method for identifying genes encoding for a specific metabolic function based on a local structure of metabolic network and multiple types of functional association evidence, including clustering of genes on the chromosome, similarity of phylogenetic profiles, gene expression, protein fusion events and others. Using E. coli and S. cerevisiae metabolic networks, we illustrate predictive ability of each individual type of association evidence and show that significantly better predictions can be obtained based on the combination of all data. In this way our method is able to predict 60% of enzyme-encoding genes of E. coli metabolism within the top 10 (out of 3551) candidates for their enzymatic function, and as a top candidate within 43% of the cases.ConclusionWe illustrate that a combination of genome context and other functional association evidence is effective in predicting genes encoding metabolic enzymes. Our approach does not rely on direct sequence homology to known enzyme-encoding genes, and can be used in conjunction with traditional homology-based metabolic reconstruction methods. The method can also be used to target orphan metabolic activities.
2498239
Automatic threshold decision of background registration technique for video segmentation
Background registration technique is useful to solve still object problem and uncovered background problem for video segmentation. However, it is hard to automatically decide the threshold of the frame difference for background registration to make it more feasible for real-time applications. Many previous works made efforts on automatic threshold decision for change detection. In this paper, we propose a new method of automatic threshold decision algorithm in a totally different viewpoint. Not only change detection but also the quantization effect in discrete domain is concerned. A Gaussianity test is first applied to find the standard deviation of Gaussian noise from the camera. Then, the quantization effect in discrete domain is taken into consideration to derive the relation between the standard deviation and the optimal threshold value. A couple MPEG-4 sequences and experimental sequences are tested as examples. Simulation results show that the calculated threshold values are suitable for background registration to give good segmentation results.