47,110 research outputs found

    Content-Based Image Retrieval using SURF and Colour Moments

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    Content-Based Image Retrieval (CBIR) is a challenging task which retrieves the similar images from the large database. Most of the CBIR system uses the low-level features such as colour, texture and shape to extract the features from the images. In Recent years the Interest points are used to extract the most similar images with different view point and different transformations. In this paper the SURF is combined with the colour feature to improve the retrieval accuracy. SURF is fast and robust interest points detector/descriptor which is used in many computer vision applications. To improve the performance of the system the SURF is combined with Colour Moments since SURF works only on gray scale images. The KD-tree with the Best Bin First (BBF) search algorithm is to index and match the similarity etween the features of the images. Finally, Voting Scheme algorithm is used to rank and retrieve the matched images from the database

    Neural network-based shape retrieval using moment invariants and Zernike moments.

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    Shape is one of the fundamental image features for use in Content-Based Image Retrieval (CBIR). Compared with other visual features such as color and texture, it is extremely powerful and provides capability for object recognition and similarity-based image retrieval. In this thesis, we propose a Neural Network-Based Shape Retrieval System using Moment Invariants and Zernike Moments. Moment Invariants and Zernike Moments are two region-based shape representation schemes and are derived from the shape in an image and serve as image features. k means clustering is used to group similar images in an image collection into k clusters whereas Neural Network is used to facilitate retrieval against a given query image. Neural Network is trained by the clustering result on all of the images in the collection using back-propagation algorithm. In this scheme, Neural Network serves as a classifier such that moments are inputs to the Neural Network and the output is one of the k classes that have the largest similarities to the query image. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .C444. Source: Masters Abstracts International, Volume: 44-03, page: 1396. Thesis (M.Sc.)--University of Windsor (Canada), 2005

    Acquisition of Images using Neural Network

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    The application of computer vision to the image retrieval problem is Content-based image retrieval (CBIR). The interest in digital images is growing day by day. Users in professional fields are make use of the opportunities offered by the ability to access and manipulate remotely-stored images in different ways. The problems in image retrieval are becoming widely accepted, and the finding solution is an active area for research and development. This dissertation work aims at developing a hybrid scheme for intelligent image retrieval system using neural networks. Each image in the database is indexed by a visual feature vector, which is extracted using color moments and discrete cosine transform coefficients. The query is characterized by a set of predefined semantic labels. A novel method of similarity measure using dot product is used for ranking and retrieval for improved performance of the system DOI: 10.17762/ijritcc2321-8169.15050

    Facilitating Image and Document Retrieval Using Image Content and Querying Keyword

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    Evolution of ubiquitous computing in the areas of personal computing technology has produced staggeringly large data It is difficult to search mainly the image data by understanding   users objective only by keywords and phrases and this leads to uncertain outcomes. For producing these outcomes effectively, this paper introduces a new approach to the problem of image learning to enable search engines to learn about visual content over time based on user feedback through one click activity and images from a pool recovered by text based query are re-ranked depending on both visual and text based query. Content Based Image Retrieval (CBIR) techniques are used for accessing semantically-relevant images from an image data source depending on automatically-derived image functions for features like Geometric moments, Global histogram, Color Moments, Local histogram. Documents can also be retrieved using the text based query by the user

    Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval

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    The aim of a Content-Based Image Retrieval (CBIR) system, also known as Query by Image Content (QBIC), is to help users to retrieve relevant images based on their contents. CBIR technologies provide a method to find images in large databases by using unique descriptors from a trained image. The image descriptors include texture, color, intensity and shape of the object inside an image. Several feature-extraction techniques viz., Average RGB, Color Moments, Co-occurrence, Local Color Histogram, Global Color Histogram and Geometric Moment have been critically compared in this paper. However, individually these techniques result in poor performance. So, combinations of these techniques have also been evaluated and results for the most efficient combination of techniques have been presented and optimized for each class of image query. We also propose an improvement in image retrieval performance by introducing the idea of Query modification through image cropping. It enables the user to identify a region of interest and modify the initial query to refine and personalize the image retrieval results.Comment: 8 pages, 16 figures, 11 table

    AN OPTIMIZED FEATURE EXTRACTION TECHNIQUE FOR CONTENT BASED IMAGE RETRIEVAL

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    Content-based image retrieval (CBIR) is an active research area with the development of multimedia technologies and has become a source of exact and fast retrieval. The aim of CBIR is to search and retrieve images from a large database and find out the best match for the given query. Accuracy and efficiency for high dimensional datasets with enormous number of samples is a challenging arena. In this paper, Content Based Image Retrieval using various features such as color, shape, texture is made and a comparison is made among them. The performance of the retrieval system is evaluated depending upon the features extracted from an image. The performance was evaluated using precision and recall rates. Haralick texture features were analyzed at 0 o, 45 o, 90 o, 180 o using gray level co-occurrence matrix. Color feature extraction was done using color moments. Structured features and multiple feature fusion are two main technologies to ensure the retrieval accuracy in the system. GIST is considered as one of the main structured features. It was experimentally observed that combination of these techniques yielded superior performance than individual features. The results for the most efficient combination of techniques have also been presented and optimized for each class of query

    Improving performance of content based image retrieval system with color features

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    Content based image retrieval (CBIR) encompasses a variety of techniques with a goal to solve the problem of searching for digital images in a large database by their visual content. Applications where the retrieval of similar images plays a crucial role include personal photo and art collections, medical imaging, multimedia publications and video surveillance. Main objective of our study was to try to improve the performance of the query-by-example image retrieval system based on texture features – Gabor wavelet and wavelet transform – by augmenting it with color information about the images, in particular color histogram, color autocorrelogram and color moments. Wang image database comprising 1000 natural color images grouped into 10 categories with 100 images was used for testing individual algorithms. Each image in the database served as a query image and the retrieval performance was evaluated by means of the precision and recall. e number of retrieved images ranged from 10 to 80. e best CBIR performance was obtained when implementing a combination of all 190 texture- and color features. Only slightly worse were the average precision and recall for the texture- and color histogram-based system. is result was somewhat surprising, since color histogram features provide no color spatial informa- tion. We observed a 23% increase in average precision when comparing the system containing a combination of texture- and all color features with the one consisting of exclusively texture descriptors when using Euclidean distance measure and 20 retrieved images. Addition of the color autocorrelogram features to the texture de- scriptors had virtually no e ect on the performance, while only minor improvement was detected when adding rst two color moments – the mean and the standard deviation. Similar to what was found in the previous studies with the same image database, average precision was very high in case of dinosaurs and owers and very low with beach, food, monuments and mountains images
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