Reducing noise in image query processing is no doubt one of the key elements to achieve high retrieval effectiveness. However, existing techniques are not able to eliminate noise from similarity matching since they capture the features of the entire image are or pre-perceived objects at the database build time. In this paper we address this outstanding issue by proposing a similarity mode for noise- free queries. In our approach, users formulate their queries by specifying objects of interest, and image similarity is based only on these relevant objects. We discuss how our approach can handle translation and scaling matching as well as how space overhead can be minimized. Our experiments show that this approach, with 1/16 the storage overhead, outperforms techniques for rectangular queries and a related technique by a significant margin.
Most content-based image retrieval techniques are not able to eliminate noise from similarity matching since they capture the features of the entire image area or pre- perceived objects at the database build time. Recent approaches address this outstanding issue by allowing users to arbitrarily exclude noise in formulating their queries. This capability has resulted in high retrieval effectiveness for a wide range of queries. However, implementing these techniques for large image collections presents a great challenge since we can not assume any shape for queries defined by users. In this paper, we propose an efficient indexing/retrieval technique for arbitrarily-shaped queries which is able to eliminate a majority of unqualified images. Moreover, we improve the retrieval process with a filtering phase to prune out additional false matches before the detailed similarity measure is carried out. We have implemented the proposed technique in our image retrieval system for a large image collection. Our experimental results show that our technique is capable of handling image matching very well and 70 times on average faster than the straightforward sequential scanning.
Shot boundary detection (SBD) is the first fundamental step to managing video databases. It segments video data into the basic units for indexing and retrieval. Many automatic SBD techniques exist. They, however, are based on sequential search, and therefore too expensive for practical use. To address this problem, we explore a different direction to SBD in this paper. We investigate a non-linear approach in which most video frames do not need to be compared. This idea is fundamentally different from all existing methods. In fact, it is orthogonal to these schemes in the sense that it can be applied to substantially improve their performance. Our experiments show that this idea speeds up a conventional method based on color histograms up to 16 times while preserving the same accuracy. On the average, the improvement is five time according to our experiments on 26 videos of six different types.
Patching has been shown to be cost efficient for video-on- demand systems. Unlike conventional multicast, patching is a dynamic multicast scheme which enables a new request to join an ongoing multicast. Since a multicast can now grow dynamically to serve new users, this approach is more efficiency than traditional multicast. In addition, since a new request can be serviced immediately without having to wait for the next multicast, true video-on-demand can be achieved. In this paper, we introduce the notion of patching window, and present a generalized patching method. We show that existing schemes are special cases with a specific patching window size. We derive a mathematical formula to help determine the optimal size for the patching window. This formula allows us to design the best patching scheme given a workload. The proposed technique is validated using simulations. They show that the analytical results are very accurate. We also provide performance results to demonstrate that the optimal technique outperforms the existing schemes by a significant margin. It is also up to two times better than the best Piggybacking method which provides data sharing by merging the services in progress into a single stream by altering their display rates.
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