Image data-deduplication using the block truncation coding technique

According to the recent literature, it is pragmatic that image storage and retrieval through the World Wide Web (WWW) has made impressive progress. Practical searching for image still confronts us with present retrieval systems. A Content Based Image Retrieval (CBIR) system provides an efficient way of retrieving related images from image collections. In this paper we present a new technique to extract the images from the database to achieve better performance and efficiency. The novel method uses an approach, which combines texture information and modified Block Truncation Coding (BTC) method to extract the color features from the image. The whole image is divided into a fixed block of size 4 × 4, 8 × 8, 16 × 16, 32 × 32, 64 × 64, 128 × 128 and 256 × 256 non overlapped blocks. For each block, we calculated the threshold as a mean of the pixel values and the number of pixels which are lower than threshold value and upper than the threshold value are counted and mean of these lower and upper values have been taken and these two are considered as features. Now this process is repeated for the all blocks to extract the features of the image. The second step will compute the four texture features using the Gray-Level Co-occurrence Matrix (GLCM) as a statistical approach to extract energy, contrast, entropy and uniformity. We have used this modified BTC and texture features of the image to retrieve images. Finally, we have conducted experiments on a ground truth image database that has 1000 images of different categories. A different block sizes have been used to demonstrate the experimental results and compared with tabulation. From this we have observed that the performance is better and efficiency is low with smaller block sizes and vice versa with higher block sizes. So we have to choose the middle ranges of block sizes for better performance with better efficiency. As we have observed from the results that the performance of the retrieval system is improved because of combination of texture and color features. The proposed system's performance has been improved by an average precision of 2.41% and recall of 8.22% when compared to existing systems.

Now a days there is a fantasy to be digitized rapidly. Where digitization is applicable to every domain; no matter its technical or non-technical. So there is a need of effective retrieval of data. Today people are finding some new retrieval schemes over the traditional query based retrieval systems. So moving towards content based approach is an hour's need. Some betterments are definitely needed to empower the Content Based approach. For content based video retrieval (CBVR) system, Block Truncation Coding (BTC) is one of the techniques to extract color features of a video. One more technique is there with improvements to BTC is Thepade's Sorted Ternary Block Truncation Coding (TSTBTC). Transform feature extraction is one more technique about extraction of the video. Right now only one feature can be targeted in a technique; in other words, there is no method which can be exposed to color as well as transform feature of a video. In other hand, Vector Quantization (VQ) is a popular technique for lossy data compression. If VQ is used with TSTBTC, it can deal with color features as well as transform features of a video. Because VQ is can be operated with hybrid features. This is first time when VQ is seen in CBVR. This paper also describes the efficiency and accuracy of this technique with different Color Spaces.

Evolutions in image-data processing is rapidly changing from text-based retrieval systems to video retrieval which is created on content based systems due to its eminent demand in digital market and that can be possible only due to available multimedia data types and available bandwidth. In proposed method of Content Based Video Retrieval (CBVR) the Ordered-Dither Block Truncation Coding (ODBTC) technique is employed which generates appropriate image contents. Combinations of Void-and-cluster half-toning and Block Truncation Coding (BTC) offers low complexity in algorithm and provides better video image quality. Dither array Look-Up-Table (LUT) is a distinctive feature of ODBTC which reduces the difficulties by providing look up values of segmented blocks. ODBTC encoded streams are used for generation of two distinct features including of color features namely Color Co-occurrence Feature (CCF) and Bit Pattern Features (BPF). After quantizing and bit-mapping from ODBTC encoder, BPF is obtained by LUT. In the presented system, CBVR is achieved by Block Truncation (BT) of expected video information to be retrieved. Proposed system provides good remedy for CBVR for large digital video-data processing in the fields of Image and Video Processing.

In this study, the authors investigate content based image retrieval (CBIR) using ordered‐dither block truncation coding (ODBTC) and phase congruency feature (PCF). Relevant feature extraction plays a vital role for retrieving the image in CBIR. The unique reason to choose PCF with ODBTC is that it detects the edges and corners during variation of image while preserving image brightness and contrast. Combining the PCF and ODBTC features improves CBIR system usage in various visual data processing domains. Thus, yields a better CBIR system which assists in the reduction of storage space, decreases retrieval time and increases accuracy of the system. The precision and recall are used as performance metrics to evaluate the proposed method based on retrieval of relevant images. Extensive experimental results with Corel 1 K (1000 images), Corel 10 K (10000 images) and CALTECH 256 (30144 images) proves that the proposed method is more desirable than antecedent proposed CBIR system in terms of accuracy, precision and recall.

Internet Learning Image compression is a useful approach this is inevitable to save any snapshots in compressed sample. The compressed image is reconstructed the usage of image retrieval system for any applications. This method guarantees garage performance in various content management packages. Mainly, Internet Learning resource environment deals with greater area complexity. The e-learning storage area complexity may be reduced with the assist of image compression strategies. There are extraordinary forms of image compression techniques invented for constructing compressed assets. Block Truncation Coding (BTC) is a kind of lossy compression technique for reducing grey scale quantities the usage of blocking and quantizing stages. This is powerful technique for compressing the photos. In the scope, the want for extra active and Generative image compression methods is important to enhance BTC principles. This work proposes notably complex and novel Generative Adversarial BTC (GA-BTC) compression models with more than one variances. GABTC is developed with multi-layered Deep Neural Network (DNN) structures with GA neural models. The integration of each GA fashions and BTC standards improve the best of block constructions and reconstructions notably. The proposed work evaluates the version complexity and efficiency using diverse Internet Learning images with exclusive compression fine measurements.

As the use of digital media increases, effective retrieval and management techniques become more important. In order to extract useful information from this huge amount of data, many content based image retrieval (CBIR) systems have been developed. A typical CBIR system captures image features such as color, texture, or shape of objects in the query image and tries to retrieve images from the database with similar features. The main advantage of CBIR systems with relevance feedback is that these systems take into account the gap between the high-level concepts and low-level features and subjectivity of human perception of visual content. In this paper, a CBIR system based on color histogram has been presented and a new approach for image storage and retrieval called association-based image retrieval (ABIR) has been studied. The results of both systems have been shown.

Block truncation coding (BTC) is a well known lossy compression scheme. Due to its low complexity and easy implementation, BTC has gained wide interest in its further development and application for image compression. Based on simple thresholding, BTC retains sharp edges and thus leads to artifacts such as the staircase effect. The second problem encountered in BTC is the splitting of homogeneous regions, which produces false contours. In this work a fuzzy approach of BTC to avoid truncating homogeneous blocks and to preserve smooth edges in two-cluster blocks is proposed. Each image block, viewed as a fuzzy set, is segmented into two clusters using a fuzzy clustering algorithm. The block is then encoded by modified fuzzy weighted means of the two clusters. Initialization strategies of the fuzzy clustering algorithm and a contextual quantization method are proposed. Experimental results show an improvement of visual quality of reconstructed images and peak signal-to-noise ratio when compared to BTC, economical BTC (EBTC), absolute moment BTC (AMBTC), and a minimum mean square error quantizer (MMSEQ). Computation time required by AMBTC, EBTC, and fuzzy BTC methods are reported.

In the field of computer vision and image processing CBIR (Content based image retrieval) system become an active research area. The CBIR technique involves feature extraction based on image features (like color, shape and texture) and defining rules for comparing image. A classifier is also used by authors for image classification. For this purpose an image database taken from James Z. Wang research group website is used by many authors. Due to enormous increase in the size of database traditional methods of image retrieval by using keywords does not provide accurate result. Thus the concept of CBIR system is emerged. In last couple of years many method of feature extraction is developed but since now researcher are not able to develop a system which has gained high accuracy of human visual perception in distinguishing pictures. Therefore earlier researchers have concentrated to increase the retrieval accuracy of CBIR system by using different combination of features. The objective of this paper is to present review on a CBIR technique which combined four kinds of features set including every kind of feature for developing feature vector. Color based features (histogram), statistical features (mean, median and standard deviation), spatial features (Gabor feature and wavelet features) and texture features (GLCM) is used for forming feature vector. A SVM classifier is used with CBIR system to increase retrieval accuracy. SVM (support vector machine) is a machine learning classifier which provides good classification accuracy. Keywords: CBIR, SVM, NN, GLCM, HSV Color space, Euclidean distance matrix.

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)

In a content-based image retrieval (CBIR) system, indexing feature vectors and the similarity measure between feature vectors are two key factors for retrieval performance. We present a new CBIR system with statistical-model based image feature extraction in the wavelet domain and a Kullback divergence based similarity measure. A two component Gaussian mixture model (GMM) in the wavelet domain is employed and the model parameters are used to form features for image indexing. A new Kullback divergence based similarity measure is then presented for image retrieval. The experimental results demonstrate that the similarity measure based on the Kullback divergence is more effective than conventional similarity measures, such as the city-block distance and the Euclidean distance. It is shown that the new CBIR system, with the combination of the GMM and the new Kullback divergence based similarity measure, outperforms most other methods in retrieval performance for texture images, while keeping a comparable level of computational complexity.

Block truncation coding (BTC) is an efficient technology for color image compression. An improved BTC algorithm, luminance bit-map based BTC (LBBBTC), is presented in this paper. The LBB-BTC employs luminance bit-map to represent the three R, G and B bit-maps more exactly. Moreover, by adding 3 bits to indicate the entirely differences between Y and R, G or B bit-maps, respectively, the coding performance is improved obviously. Simulation results show that the proposed LBB-BTC performs as well as the BTC in terms of subject visual quality and PSNR, and significantly improve the compression ratio of BTC.

Content-based image retrieval (CBIR) has turned into an important and active potential research field with the advance of multimedia and imaging technology. It makes use of image features, such as color, texture and shape, to index images with minimal human intervention. A CBIR system can be used to locate medical images in large databases. In this paper we propose a CBIR system which describes the methodology for retrieving digital human brain magnetic resonance images (MRI) based on textural features and the Adaptive neuro-fuzzy inference system (ANFIS) learning to retrieve similar images from database in two categories: normal and tumoral. A fuzzy classifier has been used, because of the uncertainty in the results of classifier and capacity of learning. ANFIS is a good candidate for our categorization problem. Our proposed CBIR system can locate a query image in the category of normal or tumoral images in the online retrieval part. Finally, using a relevance feedback, we improve the effectiveness of our retrieval system. This research uses the knowledge of the CBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. We present and compare the results of the proposed method with the CBIR systems used in recent works. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency compared with the previous works.

Introduction: Content-based image retrieval (CBIR) retrieves the images from the vast image repositories. Research in the CBIR domain gets attention due to the massive number of images generated by the mobiles and various image-capturing machines. Objectives: Any image retrieval system’s primary goal is to reduce the semantic gap between low-level features and high-level perception. Methods: The CBIR techniques are classified into multiple categories based on the feature extraction and retrieval mechanism. These categories are feature-based, machine-learning-based, and deep-learning-based methods. The pioneer techniques for each category are explained in detail in this chapter. Results: The comparative analysis has been done to highlight the advantages of techniques over others. Conclusion: The various application area of the CBIR has been described. The most evolving application is the content-based medical image retrieval (CBMIR) system. The applicability of the CBMIR system in different medical science domains is explained in detail.

This paper presents an image representation and matching framework for image categorization in medical image archives. Categorization enables one to determine automatically, based on the image content, the examined body region and imaging modality. It is a basic step in content-based image retrieval (CBIR) systems, the goal of which is to augment text-based search with visual information analysis. CBIR systems are currently being integrated with picture archiving and communication systems for increasing the overall search capabilities and tools available to radiologists. The proposed methodology is comprised of a continuous and probabilistic image representation scheme using Gaussian mixture modeling (GMM) along with information-theoretic image matching via the Kullback-Leibler (KL) measure. The GMM-KL framework is used for matching and categorizing X-ray images by body regions. A multidimensional feature space is used to represent the image input, including intensity, texture, and spatial information. Unsupervised clustering via the GMM is used to extract coherent regions in feature space that are then used in the matching process. A dominant characteristic of the radiological images is their poor contrast and large intensity variations. This presents a challenge to matching among the images, and is handled via an illumination-invariant representation. The GMM-KL framework is evaluated for image categorization and image retrieval on a dataset of 1500 radiological images. A classification rate of 97.5% was achieved. The classification results compare favorably with reported global and local representation schemes. Precision versus recall curves indicate a strong retrieval result as compared with other state-of-the-art retrieval techniques. Finally, category models are learned and results are presented for comparing images to learned category models.

In this paper, we present a novel approach for medical content based image retrieval (searching for images which are pathologically similar to a given example) and demonstrate its performance on a dataset containing maxillofacial lesions. Recently, distributed databases at hospitals and CT scanning centers are related through Picture Archiving and Communication Systems (PACS). Hence, a content based image retrieval system is helpful for radiologists in medical diagnosis. In our proposed framework, a feature vector is extracted for each image and SIFT sparse codes are employed in this step. After feature extraction based on sparse coding and maximum pooling, we have utilized different similarity measures such as Euclidean norm, Manhattan distance and SVM classifier to choose most relevant images to the query image. We have evaluated our proposed framework on a dataset containing 2023 images belonging to 5 different categories: cleft, impaction, fracture, maxillary sinus cyst and healthy. Classification rate of 97.3% and precision versus recall curve indicate the effectiveness of sparse coding in content-based medical image retrieval.