A content-based parallel image retrieval system
25 Jun 2010-Vol. 1
TL;DR: A content-based parallel image retrieval system to achieve high responding ability, developed on cluster architectures, that uses the Symmetrical Color-Spatial Features (SCSF) to represent the content of an image.
Abstract: As we all know, the content-based image retrieval (CBIR) is very time-consuming due to the extraction and matching of high dimensional and complex features. The traditional CBIR systems could not respond to a very large number of retrieval requests at mean time, which are submitted from the Internet. In this paper, we propose a content-based parallel image retrieval system to achieve high responding ability. Our system is developed on cluster architectures. It has several retrieval servers to supply the service of content-based image retrieval. Our system adopts the Browser/Server (B/S) mode. The users could visit our system though web pages. Our system uses the Symmetrical Color-Spatial Features (SCSF)[25] to represent the content of an image. The SCSF is effective and efficient for image matching because it is independent of image distortion such as rotation and flip as well as it increases the matching accuracy. The SCSF was organized by M-tree[16], which could speedup the searching procedure. Our experiments show that the image matching is quickly and efficiently with the use of SCSF. And with the support of several retrieval servers, the system could respond to many users at mean time. (Abstract)
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01 Jan 1993
TL;DR: Here is a technique for automatic identification of microfossil structures and for classification of the structures according to which type of 3-D section they represent by using a specialized filter to detect local curves in the gray level image data and Hough transform processing of the resulting feature point vectors.
Abstract: Carboniferous Foraminifers are a specific type of microfossil which are manifest in plane sections of rock and are used by geologists for dating rock samples. The images contain a high degree of visual noise and currently must be interpreted by human experts. We are studying the classification problem in the context of intelligent image databases. Here we present a technique for automatic identification of microfossil structures and for classification of the structures according to which type of 3-D section they represent. This is achieved by using: (1) A specialized filter to detect local curves in the gray level image data; and (2) Hough transform processing of the resulting feature point vectors. An interesting aspect of our approach is that the processing of the features is not embedded in a program but is instead specified using a visual query language. This allows us to experiment quickly with different types of grouping criteria. The detection performance of our system is comparable with that of a trained geologist. We store the information obtained in a database together with the raw image data. The system can then present the user with only those images which contain structures of interest.
11 citations
TL;DR: A gradually focused bilinear attention model is designed to extract detailed information more effectively in fine-grained image retrieval based on sketches and outperforms the state-of-the-art sketch-based image retrieval methods.
Abstract: This paper focuses on fine-grained image retrieval based on sketches. Sketches capture detailed information, but their highly abstract nature makes visual comparisons with images more difficult. In spite of the fact that the existing models take into account the fine-grained details, they can not accurately highlight the distinctive local features and ignore the correlation between features. To solve this problem, we design a gradually focused bilinear attention model to extract detailed information more effectively. Specifically, the attention model is to accurately focus on representative local positions, and then use the weighted bilinear coding to find more discriminative feature representations. Finally, the global triplet loss function is used to avoid oversampling or undersampling. The experimental results show that the proposed method outperforms the state-of-the-art sketch-based image retrieval methods.
5 citations
TL;DR: The goal of proposed approach is to cluster relevant images using meta-heuristics in less amount of time effectively and avoids the semantic gap in image retrieval by utilizing automatic relevance feedback and meta- heuristic optimization.
Abstract: based Image Retrieval (CBIR) is the problem of searching for digital images in large databases It is the vital application of computer vision techniques to the image retrieval problem One inherent problem associated with Content based Image Retrieval is the response time of the system to retrieve relevant result from the image database The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers The parallel processing of Hadoop can be leveraged to efficiently retrieve images with very less response time The proposed approach also avoids the semantic gap in image retrieval by utilizing automatic relevance feedback and meta-heuristic optimization Automatic relevance feedback is implemented using Latent Semantic Analysis, and Particle swarm optimization provides meta-heuristic based development The goal of proposed approach is to - cluster relevant images using meta-heuristics in less amount of time effectively
3 citations
Cites background from "A content-based parallel image retr..."
...Content-based parallel image retrieval system[20] imposes parallel retrieval of images, but does not use available textual information....
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27 Jun 2016
TL;DR: A multi-page hashing scheme to search images using the image itself to not only be efficient for identical images, but similar images to some degree of fuzziness and degree of similarity as well is used.
Abstract: Involve manually tagging/annotating each image, and using traditional discrete data sorting techniques, such as hashing, to search images using the tags/annotations/groups of annotations. As of 2009, Flickr had 3.4 Billion Images, PhotoBucket had 7.2 Billion, Facebook had 15 Billion, and ImageShack had 20 Billion. However, none of these sites allow searching by image content and use other technologies, such as textual tags or the like. Image automatic annotation is still in its infancy. Google does allow searching in general by some kind of image-content description tagging, using some kind of limited-dictionary for image textual annotation. Although several high performance computing and data storage libraries exist, such as Hadoop and Spark, few are designed for fuzzy-with-some degree-of-similarity image non-textual content data-content retrieval. In this paper we use a multi-page hashing scheme to search images using the image itself to not only be efficient for identical images, but similar images to some degree of fuzziness and degree of similarity as well. The proposed technique uses Fourier descriptors as one representation of image objects as inputs to an evenly distributed and differentiable hashing scheme. One of the challenges in content-based retrieval schemes is the problem of overflow, usually expected in large databases. In the proposed method a Binary-Search-Tree (BST) scheme is used to decrease the search time within buckets and across pages when overflow occurs. Additionally, the proposed method allows for image retrieval based on either image object boundary contours (we call here Lambda search) or on object textures (we call here Lambda2 search) with identical or varying degrees of similarity. Benchmarking Results are presented to show the potential of the proposed method.
3 citations
Cites background from "A content-based parallel image retr..."
...In [10] each bucket of the hash table is a simple sorted array....
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Journal Article•
TL;DR: A new technique that extracts significant structural, texture and local edge features from images by a steady local edge response that can sustain the presence of noise, illumination changes is proposed.
Abstract: The present paper proposes a new technique that extracts significant structural, texture and local edge features from images The local features are extracted by a steady local edge response that can sustain the presence of noise, illumination changes The local edge response image is converted in to a ternary pattern image based on a local threshold The structural features are derived by extracting shapes in the form of textons The texture features are derived by constructing grey level co-occurrence matrix (GLCM) on the derived texton image A new variant of K-means clustering scheme is proposed for clustering of images The proposed method is compared with various methods of image retrieval based on data mining techniques The experimental results on Wang dataset shows the efficacy of the proposed method over the other methods
2 citations
Cites methods from "A content-based parallel image retr..."
...A content-based parallel image retrieval system to achieve high responding ability is proposed and it is based on cluster architectures [38]....
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References
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IBM1
TL;DR: The main algorithms for color texture, shape and sketch query that are presented, show example query results, and discuss future directions are presented.
Abstract: In the query by image content (QBIC) project we are studying methods to query large on-line image databases using the images' content as the basis of the queries. Examples of the content we use include color, texture, and shape of image objects and regions. Potential applications include medical (`Give me other images that contain a tumor with a texture like this one'), photo-journalism (`Give me images that have blue at the top and red at the bottom'), and many others in art, fashion, cataloging, retailing, and industry. Key issues include derivation and computation of attributes of images and objects that provide useful query functionality, retrieval methods based on similarity as opposed to exact match, query by image example or user drawn image, the user interfaces, query refinement and navigation, high dimensional database indexing, and automatic and semi-automatic database population. We currently have a prototype system written in X/Motif and C running on an RS/6000 that allows a variety of queries, and a test database of over 1000 images and 1000 objects populated from commercially available photo clip art images. In this paper we present the main algorithms for color texture, shape and sketch query that we use, show example query results, and discuss future directions.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
2,127 citations
01 Feb 1997
TL;DR: The VisualSEEk system is novel in that the user forms the queries by diagramming spatial arrangements of color regions by utilizing color information, region sizes and absolute and relative spatial locations.
Abstract: We describe a highly functional prototype system for searching by visual features in an image database. The VisualSEEk system is novel in that the user forms the queries by diagramming spatial arrangements of color regions. The system nds the images that contain the most similar arrangements of similar regions. Prior to the queries, the system automatically extracts and indexes salient color regions from the images. By utilizing e cient indexing techniques for color information, region sizes and absolute and relative spatial locations, a wide variety of complex joint color/spatial queries may be computed.
2,084 citations
"A content-based parallel image retr..." refers methods in this paper
...There have been many content-based image retrieval systems include QBIC[I], Virage[2], VisualSEEk[3], Photobook[4], Chabot[5] and VIPER[6]....
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Proceedings Article•
25 Aug 1997TL;DR: The results demonstrate that the Mtree indeed extends the domain of applicability beyond the traditional vector spaces, performs reasonably well in high-dimensional data spaces, and scales well in case of growing files.
Abstract: A new access method, called M-tree, is proposed to organize and search large data sets from a generic “metric space”, i.e. where object proximity is only defined by a distance function satisfying the positivity, symmetry, and triangle inequality postulates. We detail algorithms for insertion of objects and split management, which keep the M-tree always balanced - several heuristic split alternatives are considered and experimentally evaluated. Algorithms for similarity (range and k-nearest neighbors) queries are also described. Results from extensive experimentation with a prototype system are reported, considering as the performance criteria the number of page I/O’s and the number of distance computations. The results demonstrate that the Mtree indeed extends the domain of applicability beyond the traditional vector spaces, performs reasonably well in high-dimensional data spaces, and scales well in case of growing files.
1,792 citations
TL;DR: The Photobook system is described, which is a set of interactive tools for browsing and searching images and image sequences that make direct use of the image content rather than relying on text annotations to provide a sophisticated browsing and search capability.
Abstract: We describe the Photobook system, which is a set of interactive tools for browsing and searching images and image sequences. These query tools differ from those used in standard image databases in that they make direct use of the image content rather than relying on text annotations. Direct search on image content is made possible by use of semantics-preserving image compression, which reduces images to a small set of perceptually-significant coefficients. We discuss three types of Photobook descriptions in detail: one that allows search based on appearance, one that uses 2-D shape, and a third that allows search based on textural properties. These image content descriptions can be combined with each other and with text-based descriptions to provide a sophisticated browsing and search capability. In this paper we demonstrate Photobook on databases containing images of people, video keyframes, hand tools, fish, texture swatches, and 3-D medical data.
1,748 citations
01 Apr 1994
TL;DR: The Photobook system is described, which is a set of interactive tools for browsing and searching images and image sequences that differ from those used in standard image databases in that they make direct use of the image content rather than relying on annotations.
Abstract: We describe the Photobook system, which is a set of interactive tools for browsing and searching images and image sequences. These tools differ from those used in standard image databases in that they make direct use of the image content rather than relying on annotations. Direct search on image content is made possible by use of semantics-preserving image compression, which reduces images to a small set of perceptually significant coefficients. We describe three Photobook tools in particular: one that allows search based on gray-level appearance, one that uses 2-D shape, and a third that allows search based on textural properties.
941 citations