The Vantage Point Bees Algorithm

In this paper, an implementation of vantage point local search procedure for the Bees Algorithm (BA) in combinatorial domains is presented. In its basic version, the BA employs a local search combined with random search for both continuous and combinatorial domains. In this paper, a more robust local searching strategy namely, vantage point procedure is exploited along with random search to deal with complex combinatorial problems. This paper proposes a hybridization technique which involves the Bee Algorithm (BA) and a local search technique based on Vantage Point Tree (VPTs) construction. Following a description of the Vantage Point Bees Algorithm (VPBA), the paper presents the results obtained for several local search strategies for BA, demonstrating efficiency and robustness of the VPBA

Multiple Hashing Integration for Real-Time Large Scale Part-to-Part Video Matching

A real-time large scale part-to-part video matching algorithm, based on the cross correlation of the intensity of motion curves, is proposed with a view to originality recognition, video database cleansing, copyright enforcement, video tagging or video result re-ranking. Moreover, it is suggested how the most representative hashes and distance functions - strada, discrete cosine transformation, Marr-Hildreth and radial - should be integrated in order for the matching algorithm to be invariant against blur, compression and rotation distortions: (R; _) 2 [1; 20]_[1; 8], from 512_512 to 32_32pixels2 and from 10 to 180_. The DCT hash is invariant against blur and compression up to 64x64 pixels2. Nevertheless, although its performance against rotation is the best, with a success up to 70%, it should be combined with the Marr-Hildreth distance function. With the latter, the image selected by the DCT hash should be at a distance lower than 1.15 times the Marr-Hildreth minimum distance

DATA OUTSOURCING BASED SIMILARITY FUSION

There is a lot of advancement in the internet based strategy in a well efficient fashion respectively. Here the advancement is related to the computation of the cloud in a well oriented approach respectively. There is a lot of demand for this particular aspect from the user based aspect in a well oriented fashion respectively. Many of the users are getting attracted to this particular strategy in a well efficient fashion respectively. Here there is a user friendly oriented access of the environment followed by the quite reliable fashion respectively. There is a major problem with respect to this particular oriented strategy is the security is the major problem due to the wireless based communication of the data in a well oriented fashion respectively. Here the service provider based on the outsourcing oriented data plays a major role in the system based aspect in which query based similarity measure followed by the data metric oriented outsourcing in a well oriented fashion respectively. Here there is a provision has to be maintained in the present design oriented technique is rather well efficient format in which depending on the requirement of the user the data is directly provided for the user based access but not for the mediator oriented strategy by the dealer. Here there is a huge analysis is made n the system that is the mutual agreement is made between the user followed by the well effective service provider based fashion in which there would be maintained with the privacy or not and also the agreement involves the terms and conditions oriented aspect followed by the terms of the use with respect to the cost and the bandwidth allocation and everything there is a proper trade off followed by the negotiation based phenomena. Experiments have been conducted on the present designed technique and it performance based strategy followed by the entire system outcome is displayed in a very efficient fashion respectively

Pruning Algorithms for Low-Dimensional Non-metric k-NN Search: A Case Study

We focus on low-dimensional non-metric search, where tree-based approaches permit efficient and accurate retrieval while having short indexing time. These methods rely on space partitioning and require a pruning rule to avoid visiting unpromising parts. We consider two known data-driven approaches to extend these rules to non-metric spaces: TriGen and a piece-wise linear approximation of the pruning rule. We propose and evaluate two adaptations of TriGen to non-symmetric similarities (TriGen does not support non-symmetric distances). We also evaluate a hybrid of TriGen and the piece-wise linear approximation pruning. We find that this hybrid approach is often more effective than either of the pruning rules. We make our software publicly available

A new dynamic indexing structure for searching time-series patterns

We target at the growing topic of representing and searching time series data. A new MABI (Moving Average Based Indexing) technique is proposed to improve the performance of the similarity searching in large timeseries databases. Notions of Moving average and Euclidean distances are introduced to represent the time-series data and to measure the distance between two series. Based on the distance reducing rate relation theorem, the MABI technique has the ability to prune the unqualified sequences out quickly in similarity searches and to restrict the search to a much smaller range, compare to the data in question. Finally the paper reports some results of the experiment on a stock price data set, and shows the good performance of MABI method