20424930 knowledge data mining ieee project titles with abstract 2009 2010

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IEEE Project Titles

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DOMAIN : IEEE KNOWLEDGE AND DATA MINING

Modeling Image Data for Effective Indexing and Retrieval in Large General Image Databases

Abstract: we propose an image semantic model based on the knowledge and criteria in the field of linguistics and taxonomy. Our work bridges the “semantic gap” by seamlessly exploiting the synergy of both visual feature processing and semantic relevance computation in a new way, and provides improved query efficiency and effectiveness for large general image databases. Our main contributions are as follows: We design novel data structures, namely, a Lexical Hierarchy, an Image-Semantic Hierarchy, and a number of Atomic Semantic Domains, to capture the semantics and the features of the database, and to provide the indexing scheme. We present a novel image query algorithm based on the proposed structures. In addition, we propose a novel term expansion mechanism to improve the lexical processing. Our extensive experiments indicate that our proposed techniques are effective inachieving high runtime performance with improved retrieval accuracy. The experiments also show that the proposed method has good scalability.

JAVA



Towards Suspicious Behavior Discovery in Video Surveillance System

Abstract: Video surveillance systems are becoming common in commercial, industrial, and residential environments. The systems in used are constructed mainly by hard devices with no or very few soft intelligence. It is difficult for human to recognize important events as they happening and to control over unwilling situations by staring at the screens all the time. Soft intelligence to identify human behaviors in the surveillance systems is expected. A systempsilas architecture for this goal is presented in this paper. Bottom-up processing methods and top-down design schemes are integrated in the architecture. The integration may increase the accuracy of relevance algorithms and reduce the computing cost. The feasibility of the system is assured.

JAVA


A Relation-Based Page Rank Algorithm for Semantic Web Search Engines


Abstract: With the tremendous growth of information available to end users through the Web, search engines come to play ever a more critical role. Nevertheless, because of their general-purpose approach, it is always less uncommon that obtained result sets provide a burden of useless pages. The next-generation Web architecture, represented by the Semantic Web, provides the layered architecture possibly allowing overcoming this limitation. Several search engines have been proposed, which allow increasing information retrieval accuracy by exploiting a key content of semantic Web resources, that is, relations. However, in order to rank results, most of the existing solutions need to work on the whole annotated knowledge base. In this paper, we propose a relation-based page rank algorithm to be used in conjunction with semantic Web search engines that simply relies on information that could be extracted from user queries and on annotated resources. Relevance is measured as the probability that a retrieved resource actually contains those relations whose existence was assumed by the user at the time of query definition.

JAVA


Fast Query Point Movement Techniques for Large CBIR Systems

Abstract: Target search in content-based image retrieval (CBIR)


systems refers to finding a specific (target) image such as a particular registered logo or a specific historical photograph. Existing techniques, designed around query refinement based on relevance feedback, suffer from slow convergence, and do not guarantee to find intended targets. To address these limitations, we propose several efficient query point movement methods. We prove that our approach is able to reach any given target image with fewer iterations in the worst and average cases. We propose a new index structure and query processing technique to improve retrieval effectiveness and efficiency. We also consider strategies to minimize the effects of users’ inaccurate relevance feedback. Extensive experiments in simulated and realistic environments show that our approach significantly reduces the number of required iterations and improves overall retrieval performance. The experimental results also confirm that our approach can always retrieve intended targets even with poor selection of initial query points.

JAVA


Evaluating the Effectiveness of Personalized Web Search

Abstract: Although personalized search has been under way for many years and many personalization algorithms have been investigated, it is still unclear whether personalization is consistently effective on different queries for different users and under different search


contexts. In this paper, we study this problem and provide some findings. We present a large-scale evaluation framework for personalized search based on query logs and then evaluate five personalized search algorithms (including two click-based ones and three topical-interest-based ones) using 12-day query logs of Windows Live Search. By analyzing the results, we reveal that personalized Web search does not work equally well under various situations. It represents a significant improvement over generic Web search for some queries, while it has little effect and even harms query performance under some situations. We propose click entropy as a simple measurement on whether a query should be personalized. We further propose several features to automatically predict when a query will benefit from a specific personalization algorithm. Experimental results show that using a personalization algorithm for queries selected by our prediction model is better than using it simply for all queries.

JAVA


Batch Mode Active Learning with Applications to Text Categorization and Image Retrieval

Abstract: Most machine learning tasks in data classification and information retrieval require manually labeled data examples in the training stage. The goal of active learning is to select the most 6/1, 1st Floor, Gokulae Street, Ramnagar, Coimbatore- 641 009. Phone : 0422- 4377414 Mobile : 9894604623 Mail: [email protected] Web: www.brainrichtech.com

informative examples for manual labeling in these learning tasks. Most of the previous studies in active learning have focused on selecting a single unlabeled example in each iteration. This could be inefficient, since the classification model has to be retrained for every acquired labeled example. It is also inappropriate for the setup of information retrieval tasks where the user's relevance feedback is often provided for the top K retrieved items. In this paper, we present a framework for batch mode active learning, which selects a number of informative examples for manual labeling in each iteration. The key feature of batch mode active learning is to reduce the redundancy among the selected examples such that each example provides unique information for model updating. To this end, we employ the Fisher information matrix as the measurement of model uncertainty, and choose the set of unlabeled examples that can efficiently reduce the Fisher information of the classification model. We apply our batch mode active learning framework to both text categorization and image retrieval. Promising results show that our algorithms are significantly more effective than the active learning approaches that select unlabeled examples based only on their informativeness for the classification model.

JAVA


Predicting Missing Items in Shopping Carts

Abstract: Existing research in association mining has focused mainly on how to expedite the search for frequently co-occurring groups of items in “shopping cart” type of transactions; less attention has been paid to methods that exploit these “frequent itemsets” for prediction purposes. This paper contributes to the latter task by proposing a technique that uses partial information about the contents of a JAVA 6/1, 1st Floor, Gokulae Street, Ramnagar, Coimbatore- 641 009. Phone : 0422- 4377414 Mobile : 9894604623 Mail: [email protected] Web: www.brainrichtech.com

shopping cart for the prediction of what else the customer is likely to buy. Using the recently proposed data structure of itemset trees (IT-trees), we obtain, in a computationally efficient manner, all rules whose antecedents contain at least one item from the incomplete shopping cart. Then, we combine these rules by uncertainty processing techniques, including the classical Bayesian decision theory and a new algorithm based on the Dempster-Shafer (DS) theory of evidence combination.


Using Association Rule Mining to Improve Semantic Web Services Composition Performance

Abstract: Web service is essential in achieving dynamic business process. With the dramatically increased number of web services advertised in UDDI, Web Portal or Internet, how to locate the best web service according to a user's requirement is becoming more and more important, which calls for efficient and effective web service discovery mechanism. Considerable efforts have been attained in solving this problem among them semantic based approaches show encouraging result. However, when several semantically equivalent web service candidates are returned by matchmaking process, how to discern which one is the most suitable one is a real challenge. This

JAVA


paper presents a framework by which provider's context and user's context has been explored to help understand the real need of a user. This framework uses association rule for context modeling of provider and user from web service composition perspective. After modeling context, a ranking service is invoked to compare web service candidates with user's requirement and then the best suitable potential web service will be selected.


Biometric Authentication for Border Control Applications

Abstract: We propose an authentication methodology that combines multimodal biometrics and cryptographic mechanisms for border control applications. We accommodate faces and fingerprints without a mandatory requirement of (tamper-resistant) smart-card-level devices on e-passports for easier deployment. It is even allowable to imprint (publicly readable) bar codes on the passports. Additionally, we present a solution based on the certification and key management method to control the validity of passports within the current Public-Key Infrastructure (PKI) technology paradigm.



Desktop search in the intranet with integrated desktop search engines

Abstract: The desktop search is a technology more and more popular in computer experience. A desktop search software, like Google desktop search, could offer us a quick service for searching information by building sorted index for the files, images, and other catalogs. But for enterprise, it is not enough only to search in local computer. Sometimes the useful information like meeting minutes or schedule of certain works may be concealed in other personpsilas computer in the intranet. Therefore, itpsilas a good idea to share the desktop search function to other members in intranet. Besides, there are a variety of desktop search engines and operating systems installed in the office. So, this paper presents the search engine independent and even the OS independent mechanism to achieve desktop search in the whole intranet with integrated desktop search engines.



A Least Grade Page Replacement Algorithm for Web Cache Optimization

Abstract: Caching is a technique first used by memory management to reduce bus traffic and latency of data access. Web traffic has increased tremendously since the beginning of the 1990 s. With the significant increase of Web traffic, caching techniques are applied to Web caching to reduce network traffic, user-perceived latency, and server load by caching the documents in local proxies. In this paper, we analyzed both advantages and disadvantages of some current Web cache replacement algorithms including lowest relative value algorithm, least weighted usage algorithm and least unified-value (LUV) algorithm. Based on our analysis, we proposed a new algorithm, called least grade replacement (LGR), which takes recency, frequency, perfect-history, and document size into account for Web cache optimization. The optimal recency coefficients were determined by using 2- and 4-way set associative caches. The cache size was varied from 32 k to 256 k in the simulation. The simulation results showed that the new algorithm (LGR) is better than LRU and LFU in terms of hit ratio (BR) and byte hit ratio (BHR).



Classifying search queries using the Web as a source of knowledge

Abstract: We propose a methodology for building a robust query classification system that can identify thousands of query classes, while dealing in real time with the query volume of a commercial Web search engine. We use a pseudo relevance feedback technique: given a query, we determine its topic by classifying the Web search results retrieved by the query. Motivated by the needs of search advertising, we primarily focus on rare queries, which are the hardest from the point of view of machine learning, yet in aggregate account for a considerable fraction of search engine traffic. Empirical evaluation confirms that our methodology yields a considerably higher classification accuracy than previous

Java


20424930 knowledge data mining ieee project titles with abstract 2009 2010

Documents

imagesemantic hierarchy

image semantic model

novel image query algorithm

ieee knowledge

modeling image data

generation web architecture

data mining

semantic relevance computation