Irrelevant frame removal for scene analysis using video hyperclique pattern and spectrum analysis

<p>Video often include frames that are irrelevant to the scenes for recording. These are mainly due to imperfect shooting, abrupt movements of camera, or unintended switching of scenes. The irrelevant frames should be removed before the semantic analysis of video scene is performed for video retrieval. An unsupervised approach for automatic removal of irrelevant frames is proposed in this paper. A novel log-spectral representation of color video frames based on Fibonacci lattice-quantization has been developed for better description of the global structures of video contents to measure similarity of video frames. Hyperclique pattern analysis, used to detect redundant data in textual analysis, is extended to extract relevant frame clusters in color videos. A new strategy using the k-nearest neighbor algorithm is developed for generating a video frame support measure and an h-confidence measure on this hyperclique pattern based analysis method. Evaluation of the proposed irrelevant video frame removal algorithm reveals promising results for datasets with irrelevant frames.</p>

Pattern Preserving Clustering

Clustering and association analysis are important techniques for analyzing data. Cluster analysis (Jain & Dubes, 1988) provides insight into the data by dividing objects into groups (clusters), such that objects in a cluster are more similar to each other than to objects in other clusters. Association analysis (Agrawal, Imielinski & Swami, 1993), on the other hand, provides insight into the data by finding a large number of strong patterns -- frequent itemsets and other patterns derived from them -- in the data set. Indeed, both clustering and association analysis are concerned with finding groups of strongly related objects, although at different levels. Association analysis finds strongly related objects on a local level, i.e., with respect to a subset of attributes, while cluster analysis finds strongly related objects on a global level, i.e., by using all of the attributes to compute similarity values. Recently, Xiong, Tan & Kumar (2003) have defined a new pattern for association analysis -- the hyperclique pattern -- which demonstrates a particularly strong connection between the overall similarity of all objects and the itemsets (local pattern) in which they are involved. The hyperclique pattern possesses a high affinity property: the objects in a hyperclique pattern have a guaranteed level of global pairwise similarity to one another as measured by the cosine similarity (uncentered Pearson correlation coefficient). Since clustering depends on similarity, it seems reasonable that the hyperclique pattern should have some connection to clustering. Ironically, we found that hyperclique patterns are mostly destroyed by standard clustering techniques, i.e., standard clustering schemes do not preserve the hyperclique patterns, but rather, the objects comprising them are typically split among different clusters. To understand why this is not desirable, consider a set of hyperclique patterns for documents. The high affinity property of hyperclique patterns requires that these documents must be similar to one another; the stronger the hyperclique, the more similar the documents. Thus, for strong patterns, it would seem desirable (from a clustering viewpoint) that documents in the same pattern end up in the same cluster in many or most cases. As mentioned, however, this is not what happens for traditional clustering algorithms. This is not surprising since traditional clustering algorithms have no built in knowledge of these patterns and may often have goals that are in conflict with preserving patterns, e.g., minimize the distances of points from their closest cluster centroids.

Mining Hyperclique Patterns

This chapter presents a framework for mining highly-correlated association patterns named hyperclique patterns. In this framework, an objective measure called h-confidence is applied to discover hyperclique patterns. We prove that the items in a hyperclique pattern have a guaranteed level of global pairwise similarity to one another. Also, we show that the h-confidence measure satisfies a cross-support property which can help efficiently eliminate spurious patterns involving items with substantially different support levels. In addition, an algorithm called hyperclique miner is proposed to exploit both cross-support and anti-monotone properties of the h-confidence measure for the efficient discovery of hyperclique patterns. Finally, we demonstrate that hyperclique patterns can be useful for a variety of applications such as item clustering and finding protein functional modules from protein complexes.

Approaches for Semantic Association Mining and Hidden Entities Extraction in Knowledge Base

Due to the rapidly increasing use of information and communications technology, Semantic Web technology is being increasingly applied in a large spectrum of applications in which domain knowledge is represented by means of an ontology in order to support reasoning performed by a machine. A semantic association (SA) is a set of relationships between two entities in knowledge base represented as graph paths consisting of a sequence of links. Because the number of relationships between entities in a knowledge base might be much greater than the number of entities, it is recommended to develop tools and invent methods to discover new unexpected links and relevant semantic associations in the large store of the preliminary extracted semantic association. Semantic association mining is a rapidly growing field of research, which studies these issues in order to create efficient methods and tools to help us filter the overwhelming flow of information and extract the knowledge that reflect the user need. The authors present, in this work, an approach which allows the extraction of association rules (SWARM: Semantic Web Association Rule Mining) from a structured semantic association store. Then, present a new method which allows the discovery of relevant semantic associations between a preliminary extracted SA and predefined features, specified by user, with the use of Hyperclique Pattern (HP) approach. In addition, the authors present an approach which allows the extraction of hidden entities in knowledge base. The experimental results applied to synthetic and real world data show the benefit of the proposed methods and demonstrate their promising effectiveness.