| Summary: | 碩士 === 國立臺灣海洋大學 === 資訊工程學系 === 95 === This paper presents an object-based image retrieval using a method based on perceptual grouping of image regions using the modified generalized Hough transform. The effectiveness of region-based representation for content-based image retrieval is extensively studied in the literature. One common weakness of the region-based approaches is that the homogeneous image regions have little correspondence to the semantic image concepts, thus, the retrieval results of region-based approaches in terms of regions’ low-level visual features are far from satisfactory. It is desirable and yet remains as a challenge for querying multimedia data by finding an object inside a target image. Given an object model, an added difficulty is that the object might be translated, rotated, and scaled inside a target image. Object segmentation and recognition is the primary step of computer vision for applying to image retrieval of higher-level image analysis. However, automatic segmentation and recognition of objects via object models is a difficult task without a priori knowledge about the shape of objects. Instead of segmentation and detailed object representation, the objective of this research is to develop and apply computer vision methods that explore the structure of an image object by perceptual grouping of image regions to retrieve images from a database. A voting scheme based on generalized Hough transform is proposed to provide object search method, which is invariant to the translation, rotation, scaling of image data, and hence, invariant to orientation and position. Computer simulation results show that the proposed method gives good performance in terms of retrieval accuracy and robustness.
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