Combining MPEG-7 based visual experts for reaching semantics

Semantic classification of images using low-level features is a challenging problem. Combining experts with different classifier structures, trained by MPEG-7 low-level color and texture descriptors is examined as a solution alternative. For combining different classifiers and features, two advanced decision mechanisms are proposed, one of which enjoys a significant classification performance improvement. Simulations are conducted on 8 different visual semantic classes, resulting in accuracy improvements between 3.5-6.5%, when they are compared with the best performance of single classifier systems.


Low-level image segmentation based scene classification
Akbaş, Emre (2010-08-26)
This paper is aimed at evaluating the semantic information content of multiscale, low-level image segmentation. As a method of doing this, we use selected features of segmentation for semantic classification of real images. To estimate the relative measure of the information content of our features, we compare the results of classifications we obtain using them with those obtained by others using the commonly used patch/grid based features. To classify an image using segmentation based features, we model th...
Automatic relevance determination for the estimation of relevant features for object recognition
Ulusoy, İlkay (2006-01-01)
Object recognition from 2D images is a highly interesting problem. The final goal is to have a system which can recognize thousands of different categories like human beings do. However, hand labelling the 2D training images in order to segment the foreground (object) from the background is a very tedious job. Because of this reason, in recent years, intelligent systems which can learn object categories from unlabelled image sets have been introduced. In this case, an image is labelled by the objects which ...
A Novel Fuzzy Visual Object Classification Approach
Altintakan, Umit Lutfu; Yazıcı, Adnan; KOYUNCU, Murat (2012-06-15)
Support Vector Machines (SVMs) have been extensively used for visual object classification to bridge the semantic gap between the low level features and high level concepts. SVM treats each training input equally during the construction of its decision surface which results in poor learning machines if training data include outliers. In this paper, a novel fuzzy visual object classification approach utilizing Self-Organizing Maps (SOMs) in SVM is proposed. The experimental results show the effectiveness of ...
Transformation of conceptual models to executable High Level Architecture federation models
Özhan, Gürkan; Oğuztüzün, Mehmet Halit S. (Springer, 2015-01-01)
In this chapter, we present a formal, declarative, and visual model transformation methodology to map a domain conceptual model (CM) to a distributed simulation architecture model (DSAM). The approach adheres to the principles of model-driven engineering (MDE). A two-phased automatic transformation strategy is delineated to translate a field artillery conceptual model (ACM) into a high-level architecture (HLA) federation architecture model (FAM). The produced model is then compiled by the code generator to ...
Reconstruction of three dimensional models from real images
Yilmaz, U; Mulayim, A; Atalay, Mehmet Volkan (2002-06-21)
An image based model reconstruction system is described. Real images of a rigid object acquired under a simple but controlled environment are used to recover the three dimensional geometry and the surface appearance. Based on a multi-image calibration method, an algorithm to extract the rotation axis of a turn-table has been developed. Furthermore, this can be extended to estimate robustly the initial bounding volume of the object to be modeled The coarse volume obtained, is then carved using a stereo corre...
Citation Formats
M. Soysal and A. A. Alatan, “Combining MPEG-7 based visual experts for reaching semantics,” VISUAL CONTENT PROCESSING AND REPRESENTATION, PROCEEDINGS, pp. 66–75, 2003, Accessed: 00, 2020. [Online]. Available: