1035628
Joint categorization of queries and clips for web-based video search
Building a video search engine on the Web is a very challenging problem. Compared with web page search, video search has its unique characteristics (such as high volume of data for each video, existence of multi-modal information including meta-data, visual content, audio, closed caption, etc). In this paper, we investigate some promising approaches to boosting the search relevance of a large scale video search engine on the Web. The contribution of our work is three-fold. (1) We developed a specialized video categorization framework which combines multiple classifiers based on different modalities. (2) By learning users' querying history and clicking log, we proposed an automatic query profile generation technique and applied the profile to query categorization. (3) A highly scalable system was developed, which integrates the online query categorization and offline video categorization. Naive Bayes with mixture of multinomials, Maximum Entropy, and Support Vector Machine categorization methods and the profile learn-ing technique were evaluated on a large scale set of video data on the Web. The evaluation of the developed system and user study has indicated that the joint categorization of queries and video data boosts the video search relevance and user search experience. The high efficiency of our approaches is also demonstrated by the good responsiveness of the system for the video search engine on the Web.
5682515
Addressing CBIR efficiency, effectiveness, and retrieval subjectivity simultaneously
This work is about Content Based Image Retrieval (CBIR), focusing on developing a Fast And Semantics-Tailored (FAST) image retrieval methodology. Specifically, the contributions of FAST methodology to the CBIR literature include: (1) development of a new indexing method based on fuzzy logic to incorporate color, texture, and shape information into a region based approach to improving the retrieval effectiveness and robustness (2) development of a new hierarchical indexing structure and the corresponding Hierarchical, Elimination-based A* Retrieval algorithm (HEAR) to significantly improve the retrieval efficiency without sacrificing the retrieval effectiveness; it is shown that HEAR is guaranteed to deliver a logarithm search in the average case (3) employment of user relevance feedbacks to tailor the semantic retrieval to each user's individualized query preference through the novel Indexing Tree Pruning (ITP) and Adaptive Region Weight Updating (ARWU) algorithms. Theoretical analysis and experimental evaluations show that FAST methodology holds a great promise in delivering fast and semantics-tailored image retrieval in CBIR.
1852867
Semantic repository modeling in image database
This work is about content based image database retrieval, focusing on developing a classification based methodology to address semantics-intensive image retrieval. With self organization map based image feature grouping, a visual dictionary is created for color, texture, and shape feature attributes, respectively. Labeling each training image with the keywords in the visual dictionary, a classification tree is built. Based on the statistical properties of the feature space we define a structure, called /spl alpha/-semantics graph, to discover the hidden semantic relationships among the semantic repositories embodied in the image database. With the /spl alpha/-semantics graph, each semantic repository is modeled as a unique fuzzy set to explicitly address the semantic uncertainty and the semantic overlap existing among the repositories in the feature space. A retrieval algorithm combining the classification tree with the fuzzy set models to deliver semantically relevant image retrieval is provided. The experimental evaluations have demonstrated that the proposed approach models the semantic relationships effectively and outperforms a state-of-the-art content based image retrieval system in the literature both in effectiveness and efficiency.
15964861
Exploiting the cognitive synergy between different media modalities in multimodal information retrieval
This is a position paper reporting an on-going collaboration project between SUNY Binghamton, USA, and Waseda University, Japan, on multimodal information retrieval through exploiting the cognitive synergy across the different modalities of the information, to facilitate an effective retrieval. Specifically, we focus on image retrieval in the applications where imagery data appear along with collateral text. It is noted that these applications are ubiquitous. We have proposed the synergistic indexing scheme (SIS) to explicitly exploit the synergy between the information of imagery and text modalities. Since the synergy we have exploited between the information of imagery and text modalities is subjective and depends on specific cognitive context, we call this type of synergy as cognitive synergy. We have reported part of the empirical evaluation and are in the process of fully implementing the SIS prototype for an extensive evaluation.
5416143
A clustering based approach to efficient image retrieval
This paper addresses the issue of effective and efficient content based image retrieval by presenting a novel indexing and retrieval methodology that integrates color, texture, and shape information for the indexing and retrieval, and applies these features in regions obtained through unsupervised segmentation, as opposed to applying them to the whole image domain. In order to address the typical color feature "inaccuracy" problem in the literature, fuzzy logic is applied to the traditional color histogram to solve for the problem to a certain degree. The similarity is defined through a balanced combination between global and regional similarity measures incorporating all the features. In order to further improve the retrieval efficiency, a secondary clustering technique is developed and employed to significantly save query processing time without compromising the retrieval precision. An implemented prototype system has demonstrated a promising retrieval performance for a test database containing 2000 general-purpose color images, as compared with its peer systems in the literature.
10911475
Stretching Bayesian Learning in the Relevance Feedback of Image Retrieval
This paper is about the work on user relevance feedback in image retrieval. We take this problem as a standard two-class pattern classification problem aiming at refining the retrieval precision by learning through the user relevance feedback data. However, we have investigated the problem by noting two important unique characteristics of the problem: small sample collection and asymmetric sample distributions between positive and negative samples. We have developed a novel approach to stretching Bayesian learning to solve for this problem by explicitly exploiting the two unique characteristics, which is the methodology of BAyesian Learning in Asymmetric and Small sample collections, thus called BALAS. Different learning strategies are used for positive and negative sample collections in BALAS, respectively, based on the two unique characteristics. By defining the relevancy confidence as the relevant posterior probability, we have developed an integrated ranking scheme in BALAS which complementarily combines the subjective relevancy confidence and the objective feature-based distance measure to capture the overall retrieval semantics. The experimental evaluations have confirmed the rationale of the proposed ranking scheme, and have also demonstrated that BALAS is superior to an existing relevance feedback method in the current literature in capturing the overall retrieval semantics.
32938113
Slotted Optical Burst Switching (SOBS) Networks
In this paper we study Optical Burst Switching (OBS) networks. Since OBS still suffers high loss ratio due to the lack of buffer at the OBS core routers, we study methods to reduce the loss without using optical buffers. We consider time-slotted OBS called Slotted OBS (SOBS), where routers are synchronized and only send fixed length bursts at the beginning of time slots. Our simulation results show that SOBS reduces the packet loss probability significantly. Moreover, we show that SOBS can be implemented with little or no additional cost and has other advantages such as the better supporting of Quality of Service (QoS).
770365
Robust head motion computation by taking advantage of physical properties
Head motion determination is an important problem for many applications including face modeling and tracking. We present a new algorithm to compute the head motion between two views from the correspondences of five feature points (eye corners, mouth corners and nose tip), and zero or more additional image point matches. The algorithm takes advantage of the physical properties of the feature points, such as symmetry, and it significantly improves the robustness of head motion estimation. This is achieved by reducing the number of unknowns to be estimated, thus increasing information redundancy. This idea can be easily extended to any number of feature point correspondences.
2290480
Optimal multicast smoothing of streaming video over an internetwork
A number of applications such as internet video broadcasts, corporate telecasts, distance learning etc. require transmission of streaming video to multiple simultaneous users across an internetwork. The high bandwidth requirements coupled with the multi-timescale burstiness of compressed video make it a challenging problem to provision network resources for transmitting streaming multimedia. For such applications to become affordable and ubiquitous, it is necessary to develop scalable techniques which can efficiently deliver streaming video to multiple heterogeneous clients across a heterogeneous internetwork. We propose using multicasting of smoothed video and differential caching of the video at intermediate nodes in the distribution tree, as techniques for reducing the network bandwidth requirements of such dissemination. We formulate the multicast smoothing problem, and develop an algorithm for computing the set of optimally smoothed transmission schedules for the tree (such that the transmission schedule along each link in the tree has the lowest peak rate and rate variability for any feasible transmission schedule for that link) given a buffer allocation to the different nodes in the tree. We also develop an algorithm to compute the minimum total buffer allocation to the entire tree and the corresponding allocation to each node, such that feasible transmission is possible to all the clients, when the tree has heterogeneous rate constraints. MPEG-2 trace-driven performance evaluations indicate that there are substantial benefits from multicast smoothing and differential caching. For example, the optimal multicast smoothing can reduce the total transmission bandwidth requirements in the distribution tree by more than a factor of 3 as compared to multicasting the unsmoothed stream.
206618709
Building an Ontology for Financial Investment
Intelligent agent technologies hold great promise for the financial services and investment industries such as portfolio management. In financial investment, a multi-agent system approach is natural because of the multiplicity of information sources and the different expertise that must be brought to bear to produce a good recommendation (such as a stock buy or sell decision). The agents in a multi-agent system need to coordinate, cooperate or communicate with each other to solve a complex problem. However, ontologies are a key component in how different agents in a multi-agent system can communicate effectively, and how the knowledge of agents can develop. This paper presents a case study in building an ontology in financial investment. The lessons we learned from the construction process are discussed. Based on our ontology development experience and the current development of ontologies, a framework of next generation ontology construction tools, which is aimed to facilitate the ontology construction, is proposed.
5191709
Discrete Time Geo/G/1 Queue with Multiple Adaptive Vacations
This paper treats the discrete time Geometric/G/1 system with vacations. In this system, after serving all customers in the system, the server will take a random maximum number of vacations before returning to the service mode. The stochastic decomposition property of steady-state queue length and waiting time has been proven. The busy period, vacation mode period, and service mode period distributions are also derived. Several common vacation policies are special cases of the vacation policy presented in this study.