Abstract
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.
Highlights
The problem of retrieving and recognizing patterns in images has been investigated for several decades by the image processing and computer vision research communities
The approach employs a multiobjective optimization (MOO) technique to find an optimal metric combining several low-level image primitives in a suitable multidescriptor space [13]
The underlying visual pattern of semantic concepts in the multifeature space are learned using the selected training set. multiobjective optimization is used to find a suitable metric in multifeature space
Summary
The problem of retrieving and recognizing patterns in images has been investigated for several decades by the image processing and computer vision research communities. Though low-level feature extraction algorithms are well understood and able to capture subtle differences between colors, statistic and deterministic textures, global color layouts, dominant color distributions, and so forth, the link between such lowlevel primitives and high-level semantic concepts remains an open problem. This approach suffers from an “averaging” effect in the structure construction process so that no much reward is added to the performance Contrasting these and other approaches from the literature, in this paper an object-oriented image retrieval approach based on image blocks is presented. The approach employs a multiobjective optimization (MOO) technique to find an optimal metric combining several low-level image primitives in a suitable multidescriptor space [13].
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