1,720,969 research outputs found
Fast exhaustive multi-resolution search algorithm based on clustering for efficient image retrieval
No full textTo find the best match for a query according to a certain similarity measure, this paper presents a fast exhaustive multi-resolution search algorithm based on image database clustering. Prior to search process, the whole image dataset is partitioned into a pre-defined number of clusters having similar feature contents. For a given query, the proposed algorithm first checks the lower bound of distances in each cluster, eliminating disqualified clusters. Next, it only examines the candidates in the surviving clusters through feature matching. To alleviate unnecessary feature-matching operations in the search procedure, the distance inequality property based on a multi-resolution data structure is employed. Simulation results show that the proposed algorithm guarantees very rapid exhaustive search. (c) 2005 Elsevier Inc. All rights reserved
A fast search algorithm for vector quantization using L-2-norm pyramid of codewords
No full textVector quantization for image compression requires expensive encoding time to find the closest codeword to the input vector. This paper presents a fast algorithm to speed up the closest codeword search process in vector quantization encoding. By using an appropriate topological structure of the codebook, we first derive a condition to eliminate unnecessary matching operations from the search procedure. Then, based on this elimination condition, a fast search algorithm is suggested. Simulation results show that with little preprocessing and memory cost, the proposed search algorithm significantly reduces the encoding complexity while maintaining the same encoding quality as that of the full search algorithm. It is also found that the proposed algorithm outperforms the existing search algorithms
Multiresolution descriptor matching algorithm for fast exhaustive search in norm-sorted databases
No full textTo determine the best match to a query image in a database, conventional content-based image retrieval schemes require the exhaustive search, where the descriptor of the query, e. g., the histogram, must be compared with literally all images in the database. However, the straightforward exhaustive search algorithm is computationally expensive. Thus, fast exhaustive search algorithms are required. We present a fast exhaustive search algorithm based on a multiresolution descriptor structure and a norm-sorted database. First, we derive a condition to eliminate unnecessary matching operations from the search procedure by using a norm-sorted structure of the database. Then, we propose a fast search algorithm based on the elimination condition, which guarantees an exhaustive search for either the best match or multiple best matches to a query. With a luminance histogram as a descriptor, we show that the proposed algorithm provides a search accuracy of 100% with a high search speed. (c) 2005 SPIE and IS&T
Automatic shot change detection algorithm using multi-stage clustering for MPEG-compressed videos
No full textAutomatic shot change detection has been recognized as an important research issue for video classification. This paper proposes an automatic clustering-based algorithm for shot change detection in MPEG-compressed videos with a small number of user-defined parameters. For accurate detection of abrupt and gradual shot changes, the proper selection and extraction of features are important. We first propose a fast edge image extraction scheme in the DCT domain on the basis of AC prediction. Then, by using the features extracted from the edge images and DC images, a two-stage clustering-based algorithm is proposed for shot change detection. In the first stage, the algorithm detects abrupt shot changes by employing two-means clustering on the 2-D feature space of histogram and pixel differences between two neighboring DC frames. In the next stage, it subsequently explores gradual shot changes between two adjacent abrupt shot changes by performing a two-step clustering scheme, which uses multiple features such as an edge energy diagram and several frame difference measures. Simulation results show that the proposed algorithm is fast and accurate. (C) 2001 Academic Press
A fast multiresolution feature matching algorithm for exhaustive search in large image databases
No full textMost of the content-based image retrieval systems require a distance computation for each candidate image in the database. As a brute-force approach, the exhaustive search can be employed for this computation. However, this exhaustive search is time-consuming and limits the usefulness of such systems. Thus, there is a growing demand for a fast algorithm which provides the same retrieval results as the exhaustive search. In this paper, me propose a fast-search algorithm based on a multiresolution data structure. The proposed algorithm computes the loa er bound of distance at each level and compares it with the latest minimum distance, starting from the low-resolution level. Once it is larger than the latest minimum distance, we can remove the candidates without calculating the full-resolution distance. By doing this, we can dramatically reduce the total computational complexity. It is noticeable that the proposed fast algorithm provides not only the same retrieval results as the exhaustive search, but also a faster searching ability than existing fast algorithms. For additional performance improvement, we can easily combine the proposed algorithm with existing tree-based algorithms. The algorithm can also be used for the fast matching of various features such as luminance histograms, edge histograms, and local binary partition textures
A rate-constrained fast full-search algorithm based on block sum pyramid
No full textThis paper presents a fast full-search algorithm (FSA) for rate-constrained motion estimation. The proposed algorithm, which is based on the block sum pyramid frame structure, successively eliminates unnecessary search positions according to rate-constrained criterion. This algorithm provides the identical estimation performance to a conventional FSA having rate constraint, while achieving considerable reduction in computation
A multi-resulution search algorithm using simple clustering for fast image retrieval in large multimedia databases
No full textA fast multi-resolution block matching algorithm and its LSI architecture for low bit-rate video coding
No full textIn this paper, we propose a fast multi-resolution block-matching algorithm (BMA) using multiple motion vector (MV) candidates and spatial correlation in MV fields, called a multi-resolution motion search algorithm (MRMCS). The proposed MRMCS satisfies high estimation performance and efficient LSI implementation. This paper describes the MRMCS with three resolution levels. At the coarsest level, two MV candidates are obtained on the basis of minimum matching error for the next search level. At the middle level, the two candidates selected at the coarsest level and the other one based on spatial MV correlation at the finest level are used as center points for local searches, and a W candidate is chosen for the next search level. Then, at the finest level, the final MV is obtained from local search around the single candidate obtained at the middle level. This paper also describes an efficient LSI architecture based on the proposed algorithm for low bit-rate video coding. Since this architecture requires small number of processing elements (PEs) and a small size on-chip memory, MRMCS can be implemented with a much smaller number of gates than other conventional architectures for full-search BMA while keeping a negligible degradation of coding performance. Moreover, the proposed motion estimator can support an advanced prediction mode (8 x 8 prediction mode) for H.263 and MPEG-4 video encoding. We implement this architecture with about 25E gates and 288 bytes of RAM for a search range of [-16.0, +15.5] by using a synthesizable VHDL
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