To support more effective video retrieval at semantic level, we introduce a novel framework to achieve semantic video classification. This novel framework includes: (a) A semantic-senstive video content representation framework via principal video shots to enhance the quality of features (i.e., the ability of the selected low-level multimodal perceptual features to discriminate among various semantic video concepts); (b) A semantic video concept interpretation framework via flexible mixture model to bridge the semantic
gap between the semantic video concepts and the low-level multimodal perceptual features; (c) A novel concept learning technique to integrate unlabeled samples with labeled samples for more accurate classifier training. Experimental results on semantic medical video classification are also presented to evaluate the performance of the proposed framework.
Many multi-dimensional index structures, such as R-Tree, R*-Tree, X-Tree, SS-Tree, VA-File, etc. have been proposed to support similarity search with l1, l2 or l(infinity ) distance as similarity measure. But they can not support such similarity search with cosine as the similarity measure. In this paper, an index structure Angle-Tree is introduced to resolve the problem. It first projects all the high dimensional points onto the unit hyper-spherical surface, i.e. normalize each original vector in the database into a unit one. Then an index structure similar to R-Tree is built for those projected points. The experimental results show that the Angle-Tree can decrease the cost of disk I/O and support fast similarity search.
In this paper, we propose a new method for indexing large amounts of points in high-dimensional space. The basic principle is as follows: Data points or feature vectors extracted from objects are first quantized into lattice points by using lattice vector quantization. Inverted file is adopted to organize those lattice points. Fast retrieval is implemented by sing the good properties of algebraic lattice. We first tested the indexing performance for range query by using lattice Eg and Hash. The initial experimental result show our method has good indexing performance. However, we found, Hash has to search the inverted file for a large amounts of lattice points if query window is bigger or dimension is high. To solve this problem, we use Trie instead of Hash and propose Tire Parallel Search Algorithm to fast access the inverted file. Further experiments have been done for n-dimensional data point by using lattice Zn. The results show the proposed index structure owns many good properties such as low CPU cost and low I/O cost in comparison to R-tree.
Traditional methods to compute color similarity based on color histogram own some disadvantages. For example, if the two images look very similar in color, their color similarity may be zero or very small because the intersection of their histograms may be null or the distance between their histograms may be very large. In this paper, we first propose a new definition of color similarity between two color images, then derive the formula of color similarity based on color histogram, which gets rid of the shortcomings of the traditional methods, at last present experimental results, which show that our method can provide satisfactory retrieval results.
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