ordered dither block truncation coding (ODBTC) employs the ordereddither technique to produce the bitmap required in traditional blocktruncationcoding (BTC). It can reduce the complexity of computations in the codi...
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ordered dither block truncation coding (ODBTC) employs the ordereddither technique to produce the bitmap required in traditional blocktruncationcoding (BTC). It can reduce the complexity of computations in the coding phrase. However, the blocking artifacts and undesired noises are still in the decoded images by ODBTC. In this paper, the properties of thresholds stored in the dither matrices are considered to estimate the decoded pixel intensities. That is, the dither matrices are used in both coding and decoding. Experimental results tell that the proposed schema can not only reduce quantization error but also improve the visual quality for the reconstructed images by ODBTC.
This paper presents a technique for Content-Based Image Retrieval (CBIR) by exploiting the low complexity advantage of the ordered-ditherblocktruncationcoding (ODBTC) for generating image content descriptors. The t...
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ISBN:
(纸本)9781479923427
This paper presents a technique for Content-Based Image Retrieval (CBIR) by exploiting the low complexity advantage of the ordered-ditherblocktruncationcoding (ODBTC) for generating image content descriptors. The two image features, namely Color Co-occurrence Feature (CCF) and Bit Pattern Features (BPF), are generated from ODBTC encoded data streams (without really performing an image compression or decoding process) to measure the similarity between two images. Experimental results show that the proposed method is superior to the blocktruncationcoding (BTC) image retrieval system and other former methods, and prove that the ODBTC scheme is not only suited for image compression for its simplicity, but also offers a conveniently way for image indexing in the content-based image retrieval system.
In recent times, exploration of multimedia required ever increasing demand and application for intelligent video retrieval from repositories. This paper presents an efficient video retrieval framework by employing the...
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In recent times, exploration of multimedia required ever increasing demand and application for intelligent video retrieval from repositories. This paper presents an efficient video retrieval framework by employing the effective singular value decomposition and computationally low complex ordered dither block truncation coding to extract simple, compact, and well discriminative Color Co-occurrence Feature (CCF). In this context, the occurrence probability of a video frame pixel in the neighborhood is employed to formulate this specific and distinct feature. Moreover, we applied a new adaptive low rank thresholding based on energy concentricity, transposition, and replacement invariance characteristics to formulate a unified fast shot boundary detection approach to solve the protuberant bottleneck problem for realtime cut and gradual transition that eventually contributes for effective keyframes extraction. Therefore, we can assert that the keyframes are distinct and discriminative to represent the whole video content. For effective indexing and retrieval, it is imperative to formulate similarity score evaluator for the encapsulated contextual video information with substantial temporal consistency, least computation, and post-processing. Therefore, we introduced graph-based pattern matching for video retrieval with an aim to sustain temporal consistency, accuracy and time overhead. Experimental results signify that the proposed method on average provides 7.40% and 17.91% better retrieval accuracy and 23.21% and 20.44% faster than the recent state-of-the-art methods for UCF11 and HMDB51 standard video dataset, respectively.
This paper presents a simple approach to improve the image retrieval accuracy in the Void-and-Cluster blocktruncationcoding compressed domain. The proposed approach directly derives an image descriptor from the Orde...
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This paper presents a simple approach to improve the image retrieval accuracy in the Void-and-Cluster blocktruncationcoding compressed domain. The proposed approach directly derives an image descriptor from the ordered dither block truncation coding (ODBTC) data stream without performing the decoding process. The Color Histogram Feature (CHF) is generated from the two ODBTC color quantizer, while the Halftoning Local Derivative Pattern (HLDP) is constructed from the ODBTC bitmap image. The similarity between two images are measured from their CHF and HLDP features. Three schemes are involved to improve the image retrieval accuracy, including the similarity weight optimization, feature reweighting, and user relevance feedback optimization. An evolutionary stochastic algorithm is exploited to optimize the similarity weight and feature weight in the nearest neighbor distance computation, as well as in the query update of relevance feedback optimization. Section 5 shows that the proposed scheme yields a promising result, and thus it can be a very effective candidate in addressing the content based image retrieval and image classification task. (C) 2015 Elsevier B.V. All rights reserved.
This paper presents a technique for content-based image retrieval (CBIR) by exploiting the advantage of low-complexity ordered-ditherblocktruncationcoding (ODBTC) for the generation of image content descriptor. In ...
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This paper presents a technique for content-based image retrieval (CBIR) by exploiting the advantage of low-complexity ordered-ditherblocktruncationcoding (ODBTC) for the generation of image content descriptor. In the encoding step, ODBTC compresses an image block into corresponding quantizers and bitmap image. Two image features are proposed to index an image, namely, color co-occurrence feature (CCF) and bit pattern features (BPF), which are generated directly from the ODBTC encoded data streams without performing the decoding process. The CCF and BPF of an image are simply derived from the two ODBTC quantizers and bitmap, respectively, by involving the visual codebook. Experimental results show that the proposed method is superior to the blocktruncationcoding image retrieval systems and the other earlier methods, and thus prove that the ODBTC scheme is not only suited for image compression, because of its simplicity, but also offers a simple and effective descriptor to index images in CBIR system.
This paper presents a new way to index a color image by exploiting the low complexity of the ordered-ditherblocktruncationcoding (ODBTC) for generating the image features. Image content descriptor is directly const...
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This paper presents a new way to index a color image by exploiting the low complexity of the ordered-ditherblocktruncationcoding (ODBTC) for generating the image features. Image content descriptor is directly constructed from two ODBTC quantizers and the corresponding bitmap image without performing the decoding process. The color co-occurrence feature (CCF) derived from the ODBTC quantizers captures the color distribution and image contrast in block based manner, while the Bit Pattern Feature (BPF) characterizes image edges and visual patterns. The similarity between two images can be easily determined based on their CCF and BPF under a specific distance metric measurement. A metaheuristic algorithm, namely Particle Swarm Optimization (PSO), is employed to find the optimum similarity constants and improve the retrieval accuracy. Experimental results demonstrate that the proposed indexing method is superior to the former blocktruncationcoding (BTC) image retrieval system and the other existing methods. The ODBTC method offers an effective way to index an image in a content-based image retrieval system, and simultaneously it is able to compress an image efficiently. Thus, this system can be a very competitive candidate in image retrieval applications. (C) 2013 Elsevier Inc. All rights reserved.
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