42 research outputs found

    A User Oriented Image Retrieval System using Halftoning BBTC

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    The objective of this paper is to develop a system for content based image retrieval (CBIR) by accomplishing the benefits of low complexity Ordered Dither Block Truncation Coding based on half toning technique for the generation of image content descriptor. In the encoding step ODBTC compresses an image block into corresponding quantizes and bitmap image. Two image features are proposed to index an image namely co-occurrence features and bitmap patterns which are generated using ODBTC encoded data streams without performing the decoding process. The CCF and BPF of an image are simply derived from the two quantizes and bitmap respectively by including visual codebooks. The proposed system based on block truncation coding image retrieval method is not only convenient for an image compression but it also satisfy the demands of users by offering effective descriptor to index images in CBIR system

    EBCOT ALGORITHM BASED INVERSE HALFTONNING ON CBIR

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    A procedure for Content Based Image Retrieval (CBIR) for the formation of picture content descriptor which abusing the upside of low multifaceted nature Order Dither Block Truncation Coding (ODBTC). The quantizer and bitmap picture are the packed type of picture got from the ODBTC method in encoding step. Translating isn't acted in this strategy. It has two picture highlight, for example, Color Co-event Feature (CCF) and Bit Pattern Feature (BPF) for ordering the picture. These highlights are straightforwardly gotten from ODBTC encoded information stream. By contrasting and the BTC picture recovery framework and other prior technique the test result show the proposed strategy is predominant. ODBTC is appropriate for picture pressure and it is a simple and viable descriptor to file the picture in CBIR. Content-based picture recovery is utilized to separate the pictures based on their substance, for example, surface, shading, shape and spatial format. To limit this hole numerous ideas was presented. Also, Images can be put away and extricated dependent on different highlights and one of the unmistakable component is Texture

    IRAbMC: Image Recommendation with Absorbing Markov Chain

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    Image Recommendation is an important feature for search engine as tremendous amount images are available online. It is necessary to retrieve relevant images to meet user's requirement. In this paper, we present an algorithm Image Recommendation with Absorbing Markov Chain (IRAbMC) to retrieve relevant images for user input query. Images are ranked by calculating keyword relevance probability between annotated keywords from log and keywords of user input query. Absorbing Markov chain is used to calculate keyword relevance. Experiments results show that the IRAbMC algorithm outperforms Markovian Semantic Indexing (MSI) method with improved relevance score of retrieved ranked images

    Batik Image Retrieval Using ODBTC Feature and Particle Swarm Optimization

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    This paper proposes an effective and efficient approach to Batik image retrieval using Ordered Dither Block Truncation Coding (ODBTC) feature. Similarity degree between two images can be easily investigated under similarity distance score between their feature descriptors. As documented in the experimental section, the feature descriptor outperforms the former existing schemes under Batik image database. The Particle Swarm Optimization (PSO) iteratively searches the optimal similarity weighting constants to further improve the image retrieval performance. Thus, a set of retrieved images become more satisfactory and acceptable for user desire and preference

    Image Recommendation Based on Keyword Relevance Using Absorbing Markov Chain and Image Features

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    Image recommendation is an important feature of search engine, as tremendous amount of images are available online. It is necessary to retrieve relevant images to meet the user's requirement. In this paper, we present an algorithm image recommendation with absorbing Markov chain (IRAbMC) to retrieve relevant images for a user's input query. Images are ranked by calculating keyword relevance probability between annotated keywords from log and keywords of user input query. Keyword relevance is computed using absorbing Markov chain. Images are reranked using image visual features. Experimental results show that the IRAbMC algorithm outperforms Markovian semantic indexing (MSI) method with improved relevance score of retrieved ranked images

    XMovie: Architecture and Implementation of a Distributed Movie System

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    We describe a system for storing, transmitting and presenting digital movies in a computer network. The hardware used in the system is standard hardware, as found in typical workstations today; no special hardware is required. The movies are shown in windows of the X window system. This allows full integration with the classical components of computer applications such as text, color graphics, menus and icons. The XMovie system has several innovative features: First, it contains a new algorithm for the gradual adaptation of the color lookup table during the presentation of the movie to ensure optimal color quality on low-end workstations. Second, it is a multi-standard system supporting the compression techniques MPEG, H.261, Motion JPEG, and a newly developed extension to the well known Color Cell Compression method. Third, we introduce AdFEC, a new adaptable forward error correction method for our movie transmission protocol

    An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System

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    The process of searching, indexing and retrieving images from a massive database is a challenging task and the solution to these problems is an efficient image retrieval system. In this paper, a unique hybrid Content-based image retrieval system is proposed where different attributes of an image like texture, color and shape are extracted by using Gray level co-occurrence matrix (GLCM), color moment and various region props procedure respectively. A hybrid feature matrix or vector (HFV) is formed by an integration of feature vectors belonging to three individual visual attributes. This HFV is given as an input to an Extreme learning machine (ELM) classifier which is based on a solitary hidden layer of neurons and also is a type of feed-forward neural system. ELM performs efficient class prediction of the query image based on the pre-trained data. Lastly, to capture the high level human semantic information, Relevance feedback (RF) is utilized to retrain or reformulate the training of ELM. The advantage of the proposed system is that a combination of an ELM-RF framework leads to an evolution of a modified learning and intelligent classification system. To measure the efficiency of the proposed system, various parameters like Precision, Recall and Accuracy are evaluated. Average precision of 93.05%, 81.03%, 75.8% and 90.14% is obtained respectively on Corel-1K, Corel-5K, Corel-10K and GHIM-10 benchmark datasets. The experimental analysis portrays that the implemented technique outmatches many state-of-the-art related approaches depicting varied hybrid CBIR system

    Detail and contrast enhancement in images using dithering and fusion

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    This thesis focuses on two applications of wavelet transforms to achieve image enhancement. One of the applications is image fusion and the other one is image dithering. Firstly, to improve the quality of a fused image, an image fusion technique based on transform domain has been proposed as a part of this research. The proposed fusion technique has also been extended to reduce temporal redundancy associated with the processing. Experimental results show better performance of the proposed methods over other methods. In addition, achievements have been made in terms of enhancing image contrast, capturing more image details and efficiency in processing time when compared to existing methods. Secondly, of all the present image dithering methods, error diffusion-based dithering is the most widely used and explored. Error diffusion, despite its great success, has been lacking in image enhancement aspects because of the softening effects caused by this method. To compensate for the softening effects, wavelet-based dithering was introduced. Although wavelet-based dithering worked well in removing the softening effects, as the method is based on discrete wavelet transform, it lacked in aspects like poor directionality and shift invariance, which are responsible for making the resultant images look sharp and crisp. Hence, a new method named complex wavelet-based dithering has been introduced as part of this research to compensate for the softening effects. Image processed by the proposed method emphasises more on details and exhibits better contrast characteristics in comparison to the existing methods
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