6831292
Graph Cuts in Content-Based Image Classification and Retrieval with Relevance Feedback
Content-based image retrieval (CBIR) has suffered from the lack of linkage between low-level features and high-level semantics. Although relevance feedback (RF) CBIR provides a promising solution involving human interaction, certain query images poorly represented by low-level features still have unsatisfactory retrieval results. An innovative method has been proposed to increase the percentage of relevance of target image database by using graph cuts theory with the maximum-flow/minimum-cut algorithm and relevance feedback. As a result, the database is reformed by keeping relevant images while discarding irrelevant images. The relevance is increased and thus during following RF-CBIR process, previously poorly represented relevant images have higher probability to appear for selection. Better performance and retrieval results can thus be achieved.
11989987
A comprehensive simulation study of SWAN QoS model in MANETs with proactive and reactive routing
In this paper, one of the QoS models for MANETs - SWAN is studied under varying conditions to provide both quantitative and qualitative assessments of the applicability of the model in different scenarios. Results and analysis from NS-2 based simulation indicate that (i) real-time (RT) applications in SWAN-enabled mobile ad hoc networks experience low and stable delays under various traffic and mobility environment at the expense of best-effort (BE) traffic, (ii) SWAN model works compatibly with proactive as well as reactive routing protocols. A careful choice of parameters is important to balance the throughput of the BE traffic and RT traffic and QoS provisions in MANETs.
10680010
Automatic sports genre categorization and view-type classification over large-scale dataset
This paper presents a framework with two automatic tasks targeting large-scale and low quality sports video archives collected from online video streams. The framework is based on the bag of visual-words model using speeded-up robust features (SURF). The first task is sports genre categorization based on hierarchical structure. Following on the second task which is based on automatically obtained genre, views are classified using support vector machines (SVMs). As a consequence, the views classification result can be used in video parsing and highlight extraction. As compared with state-of-the-art methods, our approach is fully automatic as well as domain knowledge free and thus provides a better extensibility. Furthermore, our dataset consists of 14 sport genres with 6850 minutes in total. Both sport genre categorization and view type classification have more than 80% accuracy rates, which validate this framework's robustness and potential in web-based applications.
9865248
A security protocol for certified e-goods delivery
This paper presents an efficient security protocol for certified e-goods delivery with the following features: (1) ensures strong fairness, (2) ensures non-repudiation of origin and non-repudiation of receipt, (3) allows the receiver of an e-goods to verify, during the protocol execution, that the e-goods he is about to receive is the one he is signing the receipt for, (4) does not require the active involvement of a fully trusted third party, but rather an off-line and transparent semi-trusted third party (STTP) only in cases of unfair behaviour by any party, and (5) provides confidentiality protection for the exchanged items from the STTP.
13356041
Research on Distributed Data Mining Technology Based on Grid
Distributed data mining (DDM) techniques have become necessary for large and multi-scenario datasets requiring resources, which are heterogeneous and distributed. In this paper, we focused on distributed data mining based in grid. We have discussed and analyzed a new framework based on grid environments to execute new distributed data mining techniques on very large and distributed heterogeneous datasets. The architecture and motivation for the design have been presented. We pointed out that distributed data mining technology can offer a better solution since they are designed to work in a distributed environment by paying careful attention to the computing and communication resources.
2697231
Lossless compression of color mosaic images
We present a low complexity algorithm for lossless compression of color mosaic images generated by a Bayer CCD color filter array. This algorithm is based on an interesting use of the integer wavelet transform followed by a fast adaptive context-based Golomb-Rice coding. The lossless compression performance of the proposed algorithm is apparently the best reported in the literature so far for color mosaic images.
38464204
Practical and efficient fair document exchange over networks
This paper presents a practical and efficient protocol to support a common Internet-based e-commerce activity--fair document exchange between two parties. This protocol incorporates a novel RSA-based method for the off-line recovery of a document decryption key of a party. The principal idea for such key recovery is based on a verifiable and recoverable encryption of the document decryption key. The verifiability of the encryption allows another party to verify the correctness of the encrypted key without actually knowing the original key, and the recoverability permits a designated third party to decrypt the encrypted key to recover the original key upon a legitimate request. Such verifiable and recoverable key encryption is essential for ensuring the fairness of the exchange. The protocol presented in this paper is more practical, cost-effective and efficient than other relevant protocols designed for fair document exchange.
36024332
L-∞ constrained micronoise filtering for high-fidelity image compression
Imaging apparatus inevitably impose undesirable noises onto acquired images during real imaging process. Usually these noises are too faint to cause unpleasing visual effects, however, they degrade image fidelity and significantly lower the compression ratio of lossless coding. More baffling, in this case, there leaves little room for traditional noise filtering methods to work. This paper will introduce some of our efforts trying to weaken the effect of such Micro Noise during near-lossless compression. Experimental results on ISO test images and micro Gaussian noises demonstrate that with potentiality of filtering micro noise, an improved near-lossless coder can not only achieve obviously higher compression ratio but also provide better image fidelity (measured by mean squared error) than lossless coding.