1 edition of Retrieval of trade mark images by shape feature found in the catalog.
Retrieval of trade mark images by shape feature
|Statement||J.P. Eakins ... [et al.].|
|Series||British Library research and innovation report -- 26|
|Contributions||Eakins, J. P., British Library. Research and Innovation Centre.|
|LC Classifications||T257.V2 R47 1996|
|The Physical Object|
|Pagination||92 p. :|
|Number of Pages||92|
Image retrieval using color and shape features used for representing the color and the shape of the images in our database. 3. IMAGE DATABASE The image database used in this study was created by scanning a large number of trademarks from several books.(11 ~3) Currently, the database consists of Trademark Basics Process Overview Trademark FAQs Using Private Legal Services Non-USPTO Solicitations Madrid Protocol & international protection Application process Searching Trademarks Filing online Disclosure of Public Information Checking application status & viewing documents Responding to Office Actions Abandoned applications Ordering.
An effective solution for trademark image retrieval by combining shape description and feature matching. The authors present an efficient two-stage approach for leaf image retrieval by using simple shape features including centroid–contour distance (CCD) curve, eccentricity and angle code histogram (ACH). In the first stage, the images that are dissimilar with the query image will be first filtered out by using eccentricity to reduce the search space, and fine retrieval will follow Cited by:
In MPEG-7, image is described by its contents featured by color, texture and shape. Many works have been done in image description, they are known as Content Based Image Retrieval (CBIR). Most researches on CBIR have contributed to color/texture based indexing and retrieval. Comparatively, little work has been done on image retrieval using shape. for illustrations in books, articles, advertisements, and other media meant for the higher-level image features are preferred to lower-level ones. Similar image elements, like pixels, patches, and lines can be grouped only through the Texture, Color, Shape in content based image retrieval. Smeulders et al  in which the collection of File Size: KB.
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CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): this paper combines a number of conventional features with aspects which we believe to be novel, particularly in the application of ideas drawn from Gestalt psychology.
Until the system has been independently evaluated, it is too early to tell whether our approach to shape analysis and. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Introduction Pictorial information retrieval Research into computerized storage and retrieval of pictorial information has grown from modest beginnings in the late 's to a thriving field encompassing areas as diverse as digitized maps, satellite images, fingerprint collections, and libraries of.
Abstract. Shape retrieval from image databases is a complex problem. This paper reports an investigation on the comparative effectiveness of a number of different shape features (including those included in the recent MPEG-7 standard) and matching techniques in the retrieval of multi-component trademark by: Shape retrieval from image databases is a complex problem.
This paper reports an investigation on the comparative effectiveness of a number of different shape features (including those included in. Retrieval of Trade Mark Images By Shape Feature. By J P Eakins. Abstract. Introduction Pictorial information retrieval Research into computerized storage and retrieval of pictorial information has grown from modest beginnings in the late 's to a thriving field encompassing areas as diverse as digitized maps, satellite images Author: J P Eakins.
The proposed trademark image retrieval scheme comprises two stages: first, edge detection based on wavelet transform is performed on the trademark image, second, novel wavelet -based shape features are introduced to reflect the edge's characteristic. Abstract. Image matching is an important way to implement trademark image retrieval.
In this paper, we proposed an algorithm based on Scale Invariant Feature Transform (SIFT) for automatic trademark image search. Firstly, feature points were detected and described through calculating the gradient histogram of nearby by: 1. retrieval of abstract trademark images by shape feature.
For evaluation of the system a pilot database o f images was built. This database contained randomly selected trademark images, plus four series of test images provided by the Trade Mark Size: KB.
Abstract: The Artisan system retrieves abstract trademark images by shape similarity. It analyzes each image to characterize key shape components, grouping image regions into families that potentially mirror human image perception, and then derives characteristic indexing features from these families and from the image as a whole.
efﬁciently retrieve similar images from a database of trade mark images against a given query. This thesis examines the potential of correlation matrix memories (CMM), in particular the Ad- vanced Uncertain Reasoning Architecture (AURA), for trademark image retrieval when used.
Retrieval of Trademark Images Description: The research paper Retrieval of Trademark Images comments on retrieval of trademark images based on shape. The research paper talks about the need to have shape feature that is able to capture the human perceptual similarity in a better way.
A trademark image retrieval (TIR) system is proposed in this work to deal with the vast number of trademark images in the trademark registration system. The proposed approach commences with the extraction of edges using the Canny edge detector, performs a shape normalization procedure, and then extracts the global and local Size: KB.
We propose a new shape-based, query-by-example, image database retrieval method that is able to match a query image to one of the images in the database, based on a whole or partial match.
The proposed method has two key components: the architecture of the retrieval and the features used. Both play a role in the overall retrieval by: Content-based image retrieval CBIR makes use of image features, such as color, texture or shape, to index images with minimal human intervention.
Content-based image retrieval can. A trademark image retrieval (TIR) system is proposed in this work to deal with the vast number of trademark images in the trademark registration system.
The proposed approach commences with the extraction of edges using the Canny edge detector, performs a shape normalisation procedure, and then.
TRADEMARK RETRIEVAL Since last 15 years, there are many techniques developed which are widely accepted for the trademark image retrieval systems Trademark, STAR, ARTISAN are three of the most prominent trademark image retrieval systems.
In. The proposed retrieval technique is tested using the standard MPEG-7 shape database of images and the MPEG-7 trademark database of images. The results show 5% precision/recall improvement in the case of the MPEG-7 shape database, as well as % Bull's eye score improvement and % NMRR score improvement for the 10 randomly Author: Mohd AnuarFatahiyah, SetchiRossitza, LaiYu-Kun.
A trademark image retrieval (TIR) system is proposed in this work to deal with the vast number of trademark images in the trademark registration system. The proposed approach commences with the extraction of edges using the Canny edge detector, performs a shape normalisation procedure, and then extracts the global and local features.
Experimental results on a database of trademark images show that an integrated color- and shape-based feature representation results in 99% of the images being retrieved within the top two by: Approaches for Image Database Retrieval Based on Color, Texture, and Shape Features: /ch With an ever-increasing use and demand for digital imagery in the areas of medicine, sciences, and engineering, image retrieval is an active research area inAuthor: Kratika Arora, Ashwani Kumar Aggarwal.
At present, the technique of trademark image retrieval based on multi-feature combination of the shape mainly includes single-feature global matching or local matching and multi-feature matching, which is playing a more and more important role in the area of the trademark image retrieval.
In this paper, due to the deficiency described by some single shape-based features Cited by: 2.There are lots of diﬀerent features available that are used in image classiﬁcation and retrieval. The most common ones are color, texture and shape features .
During the last decade the content-based image retrieval (CBIR) systems have gained much popularity in many ﬁelds of industry and research [2,3]. The main.Trademark Image Retrieval: /ch With the rapid increase in the amount of registered trademarks around the world, trademark image retrieval has been developed to deal with a vast amount ofCited by: 1.