Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Combining Low-Level Features for Semantic Extraction in Image Retrieval | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 61423 12 pages doi 2007 61423 Research Article Combining Low-Level Features for Semantic Extraction in Image Retrieval Q. Zhang and E. Izquierdo Multimedia and Vision Laboratory Electronic Engineering Department Queen Mary University of London London E14NS UK Received 9 September 2006 Revised 28 February 2007 Accepted 16 April 2007 Recommended by Hyoung Joong Kim An object-oriented approach for semantic-based image retrieval is presented. The goal is to identify key patterns of specific objects in the training data and to use them as object signature. Two important aspects of semantic-based image retrieval are considered retrieval of images containing a given semantic concept and fusion of different low-level features. The proposed approach splits the image into elementary image blocks to obtain block regions close in shape to the objects of interest. A multiobjective optimization technique is used to find a suitable multidescriptor space in which several low-level image primitives can be fused. The visual primitives are combined according to a concept-specific metric which is learned from representative blocks or training data. The optimal linear combination of single descriptor metrics is estimated by applying the Pareto archived evolution strategy. An empirical assessment of the proposed technique was conducted to validate its performance with natural images. Copyright 2007 Q. Zhang and E. Izquierdo. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. INTRODUCTION The problem of retrieving and recognizing patterns in images has been investigated for several decades by the image processing and computer vision research communities. Learning approaches such as neural networks kernel machines .
