Browsing by Subject "Feature Selection"

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Open Access
Enhancing feature selection with contextual relatedness filtering using Wikipedia
(2017-08) Baydar, Melih
Feature selection is an important component of information retrieval and natural language processing applications. It is used to extract distinguishing terms for a group of documents; such terms, for example, can be used for clustering, multi-document summarization and classi cation. The selected features are not always the best representatives of the documents due to some noisy terms. Addressing this issue, our contribution is twofold. First, we present a novel approach of ltering out the noisy, unrelated terms from the feature lists with the usage of contextual relatedness information of terms to their topics in order to enhance the feature set quality. Second, we propose a new method to assess the contextual relatedness of terms to the topic of their documents. Our approach automatically decides the contextual relatedness of a term to the topic of a set of documents using co-occurrences with the distinguishing terms of the document set inside an external knowledge source, Wikipedia for our work. Deletion of unrelated terms from the feature lists gives a better, more related set of features. We evaluate our approach for cluster labeling problem where feature sets for clusters can be used as label candidates. We work on commonly used 20NG and ODP datasets for the cluster labeling problem, nding that it successfully detects relevancy information of terms to topics, and ltering out irrelevant label candidates results in signi cantly improved cluster labeling quality.
Open Access
Salient point region covariance descriptor for target tracking
(SPIE, 2013-02-22) Cakir, S.; Aytac, T.; Yildirim, A.; Behesti, S.; Gerek, O. N.; Çetin, A. Enis
Features extracted at salient points are used to construct a region covariance descriptor (RCD) for target tracking. In the classical approach, the RCD is computed by using the features at each pixel location, which increases the computational cost in many cases. This approach is redundant because image statistics do not change significantly between neighboring image pixels. Furthermore, this redundancy may decrease tracking accuracy while tracking large targets because statistics of flat regions dominate region covariance matrix. In the proposed approach, salient points are extracted via the Shi and Tomasi’s minimum eigenvalue method over a Hessian matrix, and the RCD features extracted only at these salient points are used in target tracking. Experimental results indicate that the salient point RCD scheme provides comparable and even better tracking results compared to a classical RCD-based approach, scale-invariant feature transform, and speeded-up robust features-based trackers while providing a computationally more efficient structure.