اندازه گیری شباهت ویژگی مبتنی بر گشتالت در پایگاه داده علامت تجاری
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|23027||2006||14 صفحه PDF||سفارش دهید||8360 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Pattern Recognition, Volume 39, Issue 5, May 2006, Pages 988–1001
Motivated by the studies in Gestalt principle, this paper describes a novel approach on the adaptive selection of visual features for trademark retrieval. We consider five kinds of visual saliencies: symmetry, continuity, proximity, parallelism and closure property. The first saliency is based on Zernike moments, while the others are modeled by geometric elements extracted illusively as a whole from a trademark. Given a query trademark, we adaptively determine the features appropriate for retrieval by investigating its visual saliencies. We show that in most cases, either geometric or symmetric features can give us good enough accuracy. To measure the similarity of geometric elements, we propose a maximum weighted bipartite graph (WBG) matching algorithm under transformation sets which is found to be both effective and efficient for retrieval.
o date, despite the numerous efforts in content-based image retrieval (CBIR), finding the best shape features and the best way of matching features for image retrieval remains challenging. One of the core issues is in formulating a general-purpose shape similarity measurement that guarantees good retrieval performance, with the baseline that the retrieved similar items should be consistent with human visual perception. Recently, Gestalt principle  is taken into account by researchers for the perceptual segmentation and grouping of shape features. Gestalt principle is one of the earliest studies conducted by a group of psychologists to model shape perception in the early 19th century. A number of principles have been experimentally studied and derived to govern the grouping of shape features. Perception, in general, is viewed as an active process of organization, construction and analysis. Gestalt principle emphasizes the holistic nature, where recognition is inferred more by the properties of an image as a whole, rather than parts, during visual perception. This is considered different from traditional pattern recognition where recognition is achieved by accounting image features of parts and their combinations. Take the image in Fig. 3 as an example. Gestalt principle considers white regions (areas enclosed by five group of parallel lines) as a whole as the significant property rather than the shape of six independent black regions. In this paper, we investigate the flexibility of applying Gestalt principle in trademark database since trademarks are images that usually contain rich abstract geometric features that are appropriate for the modeling of Gestalt principle. In particular, we focus on five holistic properties: symmetry, continuity, proximity, parallelism and closure derived in Gestalt principles. The first property is described by Zernike moments, while the others are extracted and represented illusively 1 as a whole by our proposed geometrical features under the weighted bipartite graph (WBG) framework , , ,  and . These five holistic properties, in general, are not effective if they are jointly integrated in a linear weighted combination way for retrieval. To solve this problem, we propose a novel adaptive selection procedure of holistic properties, which depends on the nature of a query image.
نتیجه گیری انگلیسی
Based on the five holistic properties of Gestalt principle, we have presented shape-based features that are appropriate for trademark retrieval. The effectiveness of our approach lies on the adaptive selection of features, and the maximum WBG representation for partial matching of geometrical elements inferred from Gestalt principle. Experimental results indicate that the adaptive selection scheme does improve the retrieval, in the sense that the retrieval performance using adaptive selection is better than that of using either of the two features for retrieval on their own. Experiments also show that Zernike moments work distinctly better for trademarks that have very high saliency values DS(N,M)DS(N,M), which usually refer to the highly symmetry of the trademarks, but not always the case. Also, experiments show that geometric features work reasonably well for trademarks that have describable geometric features. However, for the trademarks which are not symmetric and have no significant geometric characteristic (or simply because their geometric characteristics are difficult to be extracted by Hough transform), the retrieval performance of our approach is unsatisfactory. Future works will be concentrated on the incorporation of other feature extraction methods such as corner and texture detectors for more reliable interpretation of Gestalt principles by geometric features.