Browsing by Subject "Feature Extraction"

Now showing 1 - 3 of 3

Open Access
Classification by feature partitioning
(Springer/, 1996) Guvenir, H. A.; Şirin, İ.
This paper presents a new form of exemplar-based learning, based on a representation scheme called jfaliirf parluinning, and a panitular implementation of this technique called CFF (for Classification by feature Partioning). Learning in CFP is accomplished by storing the objects separately in each (tenure dimension as disjoint sets of values called segments A segment is; expanded through generalization or specialized by dividing in into sub-segments. Cklassification is based on a weighted voting among the individual productions of the features, which are simply the class values of the segments corresponding to the values of a test instance fur each feature An empirical evaluation of CFP and its comparison with two other classification techniques, lhai consider each feature separately are given. © 1996 Kluwer Academic Publishers,.
Open Access
Content-based retrieval of historical Ottoman documents stored as textual images
(IEEE, 2004) Şaykol, E.; Sinop, A. K.; Güdükbay, Uğur; Ulusoy, Özgür; Çetin, A. Enis
There is an accelerating demand to access the visual content of documents stored in historical and cultural archives. Availability of electronic imaging tools and effective image processing techniques makes it feasible to process the multimedia data in large databases. In this paper, a framework for content-based retrieval of historical documents in the Ottoman Empire archives is presented. The documents are stored as textual images, which are compressed by constructing a library of symbols occurring in a document, and the symbols in the original image are then replaced with pointers into the codebook to obtain a compressed representation of the image. The features in wavelet and spatial domain based on angular and distance span of shapes are used to extract the symbols. In order to make content-based retrieval in historical archives, a query is specified as a rectangular region in an input image and the same symbol-extraction process is applied to the query region. The queries are processed on the codebook of documents and the query images are identified in the resulting documents using the pointers in textual images. The querying process does not require decompression of images. The new content-based retrieval framework is also applicable to many other document archives using different scripts.
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.