37176365
A content-based scheme for CT lung image retrieval
A content based scheme to retrieve computed tomographic images (CT) of the lung is presented. It consists of a visual based user interface to allow the query be made by line drawing the interested (abnormal) regions; and a training scheme to classify the relationship between the images stored in database. The system will output a set of candidate images that are texturally similar to the query image. We marked the abnormal portions of each training image by a polygonal or rectangular shape manually because it requires expert knowledge. Then, the texture features of each marked region are extracted on the DCT or SADCT transform domain. In the training stage, the extracted DCT/SADCT coefficients are fed into a Kohonen self organizing network to find the relationship for classification. In the query stage, the system first checks which texture category the query image is in, then uses some geometrical characteristics to identify the most likely candidate image. Experimental results show that 96% of the queries can be correctly retrieved where the original image is in the candidate set.
37975436
Complexity-Distortion Optimal Search Algorithm for Block Motion Estimation
13282723
A cascading MRVQ-DCT scheme for the compression of sequence images
In this correspondence, a mean residual vector quantizer (MRVQ) is superimposed on the motion estimator for improving the intraframe coding quality of MPEG. The proposed scheme performs an MRVQ before executing the discrete cosine transformation (DCT) for the purpose of curing the DCT intrinsic trouble of blocking effect at low bit rate.
1269749
Complexity-adaptive search algorithm for block motion estimation
We propose a complexity-adaptive fast block matching algorithm design strategy that allows the user to terminate the algorithm at any target computational complexity. Two complexity-adaptive techniques, frame level complexity allocation and block level complexity allocation, are developed to approach the global complexity-distortion optimization A buffer control strategy is proposed to dynamically adjust the target complexity of one frame. The predictive complexity-distortion benefit list (PCDB list) technique is employed to allocate the target complexity into each block. By these techniques, we modify the full-search block matching, three-step search, new three-step search, and four-step search to the complexity-adaptive algorithms. Experimental results show that the complexity-adaptive algorithms could achieve better performance than traditional fixed algorithms from the viewpoint of complexity-distoftion optimization.
28811627
Recognizing depth-first-search trees in parallel
Consider that T. is a given spanning tree of an undirected graph G which contains a vertices and m (/spl ges/n-1) edges. In this paper, we propose an O(m/p+log m)-time parallel algorithm using p processors on the EREW PRAM model to determine whether T is a depth-first-search tree of G. Our algorithm is optimal in time complexity and speed-up.<<ETX>>
29712526
Three-sided side match finite-state vector quantization
Several low bit-rate still-image compression methods have been presented, such as SPHIT, hybrid VQ, and the Wu-Chen (see Proc. IEEE ICASSP, 1997) method. In particular, the image "Lena" can be compressed using less than 0.15 bpp at 31.4 dB or higher. These methods exercise the analysis techniques (wavelet or subband) before distributing the bit rate to each piece of an image, thus the dilemma between bit rate and distortion can be solved. In this paper, we propose a simple but comparable method that adopts the technique of side match VQ only. The side match vector quantization (SMVQ) is an effective VQ coding scheme at low bit-rate. The conventional side match (two-sided) VQ utilizes the codeword information of two neighboring blocks to predict the state codebook of an input vector. We propose a hierarchical three-sided side match finite-state vector quantization (HTSMVQ) method that can: (1) make the state codebook size as small as possible-the size is reduced to one if the prediction can perform perfectly; (2) improve the prediction quality for edge blocks; and (3) regularly refresh the codewords to alleviate the error propagation of side match. In the simulation results, the image "Lena" can be coded with a PSNR 34.682 dB at 0.25 bpp. It is better than SPIHT, EZW, FSSQ and hybrid VQ with 34.1, 33.17, 33.1, and 33.7 dB, respectively. At a bit rate lower than 0.15 bpp, only the enhanced version of EZW performs better than our method, at about 0.14 dB.
4227250
Content-based image retrieval by clustering
In a typical content-based image retrieval (CBIR) system, query results are a set of images sorted by feature similarities with respect to the query. However, images with high feature similarities to the query may be very different from the query in terms of semantics. This is known as the semantic gap. We introduce a novel image retrieval scheme, CLUster-based rEtrieval of images by unsupervised learning (CLUE), which tackles the semantic gap problem based on a hypothesis: semantically similar images tend to be clustered in some feature space. CLUE attempts to capture semantic concepts by learning the way that images of the same semantics are similar and retrieving image clusters instead of a set of ordered images. Clustering in CLUE is dynamic. In particular, clusters formed depend on which images are retrieved in response to the query. Therefore, the clusters give the algorithm as well as the users semantic relevant clues as to where to navigate. CLUE is a general approach that can be combined with any real-valued symmetric similarity measure (metric or nonmetric). Thus it may be embedded in many current CBIR systems. Experimental results based on a database of about 60, 000 images from COREL demonstrate improved performance.
10726926
A scalable integrated region-based image retrieval system
We present a scalable algorithm for indexing and retrieving images based on region segmentation. The method uses statistical clustering on region features and IRM (integrated region matching), a measure developed to evaluate overall similarity between images. It incorporates properties of all the regions in the images by a region-matching scheme. The algorithm has been implemented as a part of our experimental SIMPLIcity (Semantics-sensitive Integrated Matching for Picture LIbraries) image retrieval system and tested on large-scale image databases of both general-purpose images and pathology slides. Experiments have demonstrated that this technique maintains the accuracy of the original system while reducing the matching time significantly.
15532439
Scalable and accurate identification of AS-level forwarding paths
Traceroute is used heavily by network operators and researchers to identify the IP forwarding path from a source to a destination. In practice, knowing the autonomous system (AS) associated with each hop in the path is also quite valuable. In previous work we showed that the IP-to-AS mapping extracted from BGP routing tables is not sufficient for determining the AS-level forwarding paths. By comparing BGP and traceroute AS paths from multiple vantage points, Z. Morley Mao et al. (2003) proposed heuristics that identify the root causes of the mismatches and fix the inaccurate IP-to-AS mappings. These heuristics, though effective, are labor-intensive and mostly ad hoc. This paper proposes a systematic way to construct accurate IP-to-AS mappings using dynamic programming and iterative improvement. Our algorithm reduces the initial mismatch ratio of 15% between BGP and traceroute AS paths to 5% while changing only 2.9% of the assignments in the initial IP-to-AS mappings. This is in contrast to the results of Z. Morley Mao et al. (2003), where 10% of the assignments were modified and the mismatch ratio was only reduced to 9%. We show that our algorithm is robust and can yield near-optimal results even when the initial mapping is corrupted or when the number of probing sources or destinations is reduced. Our work is a key step towards building a scalable and accurate AS-level traceroute tool
14581620
Network cache model for wireless proxy caching
We propose a wireless network cache model to facilitate the cooperative proxy caching in wireless base stations. A self-configured and adaptive virtual proxy graph (VPG) is used to achieve efficient data search, data cache and data replication. Based on demand, the aggregate effect of data caching, searching and replicating actions by individual proxies automatically distributes cached Web documents closer to the interested mobile clients. Our performance study demonstrates that the proposed individual-based wireless proxy caching scheme outperforms existing caching schemes in terms of different performance metrics.
8736717
Skew detection and reconstruction based on maximization of variance of transition-counts
Abstract The input document images with skew can be a serious problem in the optical character recognition system. A robust method is proposed in this paper for skew detection and reconstruction in document images which can contain less text areas, high noises, tables, figures, flow-chart, and photos. The basic idea of our approach is the maximization of variance of transition counts for the skew detection and text-orientation determination. Once the skew angle is determined, the scanning-line model is applied to reconstruct the skew images. 103 documents with great varieties have been tested and successfully processed. The average detection time of A4 size image is 4.86 s and the reconstruction time is 5.52 s. The proposed approach is also compared with the existing algorithms published in the literature and our method gets some significant improvements in skew detection and reconstruction.