A fractal-based clustering approach in large visual database systems

Large visual database systems require effective and efficient ways of indexing and accessing visual data on the basis of content. In this process, significant features must first be extracted from image data in their pixel format. These features must then be classified and indexed to assist efficient access to image content. With the large volume of visual data stored in a visual database, image classification is a critical step to achieve efficient indexing and retrieval. In this paper, we investigate an effective approach to the clustering of image data based on the technique of fractal image coding, a method first introduced in conjunction with fractal image compression technique. A joint fractal coding technique, applicable to pairs of images, is used to determine the degree of their similarity. Images in a visual database can be categorized in clusters on the basis of their similarity to a set of iconic images. Classification metrics are proposed for the measurement of the extent of similarity among images. By experimenting on a large set of texture and natural images, we demonstrate the applicability of these metrics and the proposed clustering technique to various visual database applications.     

Low-cost image indexing for massive database

According to the trend in the modern society that utilizes various image related services and products, the amount of images created by diverse personal and industrial devices is immeasurably voluminous. The adoption of very uncommon or unique identifiers or index attributes with admissible storage requirement and adequate data representation enables massive image databases to process demanded operations effectively and efficiently. For the last decades, various content-based image retrieval techniques have been studied and contributed to support indexing in large digital image databases. However, the complexity, high processing cost of the content-based image retrieval techniques might create inefficiency regarding the configuration of a high-performance image database even though satisfying their own objectives. Moreover the indexing methods with the property of low cardinality might need additional indexing in order to provide strong uniqueness. In this paper, we present identifier generation methods for indexing which are efficient and effective in the perspective of cost and indexing performance as well. The proposed methods exploit the distribution of line segments and luminance areas in an image in order to compose identifiers with high cardinality. From the experimental evaluation, we’ve learned that our approaches are effective and efficient regarding processing time, storage requirement and indexing performance.     

Indexing chromatic and achromatic patterns for content-based colour image retrieval

In this paper, we present a method to represent achromatic and chromatic image signals independently for content-based image indexing and retrieval for image database applications. Starting from an opponent colour representation, human colour vision theories and modern digital signal processing technologies are applied to develop a compact and computationally efficient visual appearance model for coloured image patterns. We use the model to compute the statistics of achromatic and chromatic spatial patterns of colour images for indexing and content-based retrieval. Two types of colour images databases, one colour texture database and another photography colour image database are used to evaluate the performance of the developed method in content-based image indexing and retrieval. Experimental results are presented to show that the new method is superior or competitive to state-of-the-art content-based image indexing and retrieval techniques.     

Web-based image indexing and retrieval in JPEG compressed domain

As the majority of content-based image retrieval systems operate on full images in pixel domain, decompression is a prerequisite for the retrieval of compressed images. To provide a possible on-line indexing and retrieval technique for those jpg image files, we propose a novel pseudo-pixel extraction algorithm to bridge the gap between the existing image indexing technology, developed in the pixel domain, and the fact that an increasing number of images stored on the Web are already compressed by JPEG at the source. Further, we describe our Web-based image retrieval system, WEBimager, by using the proposed algorithm to provide a prototype visual information system toward automatic management, indexing, and retrieval of compressed images available on the Internet. This provides users with efficient tools to search the Web for compressed images and establish a database or a collection of special images to their interests. Experiments using texture- and colour-based indexing techniques support the idea that the proposed algorithm achieves significantly better results in terms of computing cost than their full decompression or partial decompression counterparts. This technology will help control the explosion of media-rich content by offering users a powerful automated image indexing and retrieval tool for compressed images on the Web.J. Jiang: Contacting author     

Image retrieval based on index compressed vector quantization

Increased amount of visual data in several applications necessitates content-based image retrieval. Since most of visual data is stored in compressed form, it is crucial to develop indexing techniques for searching images based on their content in compressed form. Therefore, it is desirable to explore image compression techniques with capability of describing image content in compressed form. Vector Quantization (VQ) is a compression scheme that exploits intra-block correlation and image correlation reflects image content, hence VQ is a suitable compression technique for compressed domain image retrieval.This paper introduces a novel indexing scheme for compressed domain image databases based on indices generated from IC-VQ. The proposed scheme extracts image features based on relationship between indices of IC-VQ compressed images. This relationship detects contiguous regions of compressed image based on inter- and intra-block correlation. Experimental results show effectiveness superiority of the new scheme compared to VQ and color-based schemes.     

Accurate video text detection through classification of low and high contrast images

Detection of both scene text and graphic text in video images is gaining popularity in the area of information retrieval for efficient indexing and understanding the video. In this paper, we explore a new idea of classifying low contrast and high contrast video images in order to detect accurate boundary of the text lines in video images. In this work, high contrast refers to sharpness while low contrast refers to dim intensity values in the video images. The method introduces heuristic rules based on combination of filters and edge analysis for the classification purpose. The heuristic rules are derived based on the fact that the number of Sobel edge components is more than the number of Canny edge components in the case of high contrast video images, and vice versa for low contrast video images. In order to demonstrate the use of this classification on video text detection, we implement a method based on Sobel edges and texture features for detecting text in video images. Experiments are conducted using video images containing both graphic text and scene text with different fonts, sizes, languages, backgrounds. The results show that the proposed method outperforms existing methods in terms of detection rate, false alarm rate, misdetection rate and inaccurate boundary rate.     

基于迭代分形的图象压缩和检索方法

图象所具有的海量性和无序性的特点，决定了多媒体应用的构建必须解决图象数据的高效压缩和有效检索两个关键问题，而由于传统的压缩和检索技术的研究是相互分离的，因而限制了多媒体应用系统整体性能的提高，针对此问题，从两者相互结合的观点，对图象压缩和检索方法进行了研究，首先在小波变换域内，基于迭代分形对图象数据进行压缩，然后在图象分形码的基础上，利用迭代函数系统分布特性构建的特征量来支持图象检索，实验结果验证了该方法的可行性和有效性，同时也表明了基于迭代分形的图象检索方法所具有的巨大应用潜力。     

Incremental indexing with binary feature based Tversky index using black hole entropic fuzzy clustering in cloud computing

Due to the large volume of computational and storage requirements of content based image retrieval (CBIR), outsourcing image to cloud providers become an attractive research. Even though, the cloud service provides efficient indexing of the condensed images, it remains a major issue in the process of incremental indexing. Hence, an effective incremental indexing mechanism named Black Hole Entropic Fuzzy Clustering +Deep stacked incremental indexing (BHEFC+deep stacked incremental indexing) is proposed in this paper to perform incremental indexing through the retrieval of images. The images are encrypted and stored in cloud server for ensuring the security of image retrieval process. The trained images are clustered using the clustering mechanism BHEFC based on Tversky index. With the incremental indexing process, the new training images are encrypted and are converted into the decimal form such that the weight is computed using deep stacked auto-encoder that enable to update the centroid with new score values. The experimental evaluations on benchmark datasets shows that the proposed BHEFC+deep stacked incremental indexing model achieves better results compared to the existing methods by obtaining maximum accuracy of 96.728%, maximum F-measure of 83.598%, maximum precision of 84.447%, and maximum recall of 94.817%, respectively.

     

Structural feature indexing for retrieval of partially visible shapes

Efficient and robust information retrieval from large image databases is an essential functionality for the reuse, manipulation, and editing of multimedia documents. Structural feature indexing is a potential approach to efficient shape retrieval from large image databases, but the indexing is sensitive to noise, scales of observation, and local shape deformations. It has now been confirmed that efficiency of classification and robustness against noise and local shape transformations can be improved by the feature indexing approach incorporating shape feature generation techniques (Nishida, Comput. Vision Image Understanding 73 (1) (1999) 121-136). In this paper, based on this approach, an efficient, robust method is presented for retrieval of model shapes that have parts similar to the query shape presented to the image database. The effectiveness is confirmed by experimental trials with a large database of boundary contours obtained from real images, and is validated by systematically designed experiments with a large number of synthetic data.     

Fractal indexing with the joint statistical properties and its application in texture image retrieval

Fractal image coding is a block-based scheme that exploits the self-similarity hiding within an image. Fractal parameters generated by the block-based scheme are quantitative measurements of self-similarity, and therefore they can be used to construct image signatures. By combining fractal parameters and collage error, a set of new statistical fractal signatures, such as histogram of collage error (HE), joint histogram of contrast scaling and collage error (JHSE), and joint histogram of range block mean and contrast scaling and collage error (JHMSE) is proposed. These fractal signatures effectively extract and reflect the statistical properties intrinsic in texture images. Hence, they provide new statistical features for use in texture image retrieval and identification. Furthermore, in order to reduce computational complexity of the JHMSE signature, the JHMSE signature is simplified to HM (histogram of range block mean) tJHSE and HM t HS (histogram of contrast scaling) tHE, based on the independence and distance equivalence. Mathematical analysis of the simplification scheme is also carried out. The proposed fractal signatures are compared with the existing fractal signatures. Experimental results show that the proposed signatures, HM t JHSE and HM t HS t HE, achieve a higher retrieval rate with a lower computational complexity.     

An efficient compressed domain video indexing method

Video indexing is employed to represent the features of video sequences. Motion vectors derived from compressed video are preferred for video indexing because they can be accessed by partial decoding; thus, they are used extensively in various video analysis and indexing applications. In this study, we introduce an efficient compressed domain video indexing method and implement it on the H.264/AVC coded videos. The video retrieval experimental evaluations indicate that the video retrieval based on the proposed indexing method outperforms motion vector based video retrieval in 74 % of queries with little increase in computation time. Furthermore, we compared our method with a pixel level video indexing method which employs both temporal and spatial features. Experimental evaluation results indicate that our method outperforms the pixel level method both in performance and speed. Hence considering the speed and precision characteristics of indexing methods, the proposed method is an efficient indexing method which can be used in various video indexing and retrieval applications.     

Image retrieval based on shape similarity by edge orientation autocorrelogram

This paper introduces a new feature vector for shape-based image indexing and retrieval. This feature classifies image edges based on two factors: their orientations and correlation between neighboring edges. Hence it includes information of continuous edges and lines of images and describes major shape properties of images. This scheme is effective and robustly tolerates translation, scaling, color, illumination, and viewing position variations. Experimental results show superiority of proposed scheme over several other indexing methods. Averages of precision and recall rates of this new indexing scheme for retrieval as compared with traditional color histogram are 1.99 and 1.59 times, respectively. These ratios are 1.26 and 1.04 compared to edge direction histogram.     

Intelligent multimedia data: data + indices + inference

In this paper, we present a novel approach for multimedia data indexing and retrieval that is machine independent and highly flexible for sharing multimedia data across applications. Traditional multimedia data indexing and retrieval problems have been attacked using the central data server as the main focus, and most of the indexing and query-processing for retrieval are highly application dependent. This precludes the use of created indices and query processing mechanisms for multimedia data which, in general, have a wide variety of uses across applications. The approach proposed in this paper addresses three issues: 1. multimedia data indexing; 2. inference or query processing; and 3. combining indices and inference or query mechanism with the data to facilitate machine independence in retrieval and query processing. We emphasize the third issue, as typically multimedia data are huge in size and requires intra-data indexing. We describe how the proposed approach addresses various problems faced by the application developers in indexing and retrieval of multimedia data. Finally, we present two applications developed based on the proposed approach: video indexing; and video content authorization for presentation.     

Image and video indexing using vector quantization

Visual (image and video) database systems require efficient indexing to enable fast access to the images in a database. In addition, the large memory capacity and channel bandwidth requirements for the storage and transmission of visual data necessitate the use of compression techniques. We note that image/video indexing and compression are typically pursued independently. This reduces the storage efficiency and may degrade the system performance. In this paper, we present novel algorithms based on vector quantization (VQ) for indexing of compressed images and video. To start with, the images are compressed using VQ. In the first technique, for each codeword in the codebook, a histogram is generated and stored along with the codeword. We note that the superposition of the histograms of the codewords, which are used to represent an image, is a close approximation of the histogram of the image. This histogram is used as an index to store and retrieve the image. In the second technique, the histogram of the labels of an image is used as an index to access the image. We also propose an algorithm for indexing compressed video sequences. Here, each frame is encoded in the intraframe mode using VQ. The labels are used for the segmentation of a video sequence into shots, and for indexing the representative frame of each shot. The proposed techniques not only provide fast access to stored visual data, but also combine compression and indexing. The average retrieval rates are 95% and 94% at compression ratios of 16:1 and 64:1, respectively. The corresponding cut detection rates are 97% and 90%, respectively.     

An efficient color and texture based iris image retrieval technique

This paper proposes a hierarchical approach to retrieve an iris image efficiently from for a large iris database. This approach is a combination of both iris color and texture. Iris color is used for indexing and texture is used for retrieval of iris images from the indexed iris database. An index which is determined from the iris color is used to filter out the images that are not similar to the query image in color. Further, iris texture features of those filtered images, are used to determine the images which are similar to the query image. The iris color information helps to design an efficient indexing scheme based on color indices. The color indices are computed by averaging the intensity values of all red and blue color pixels. Kd-tree is used for real-time indexing based on color indices. The iris texture features are obtained through Speeded Up Robust Features (SURF) algorithm. These features are used to get the query’s corresponding image at the top best match. The performance of the proposed indexing scheme is compared with two well known iris indexing schemes ( [Mehrotra et al., 2010] and [Puhan and Sudha, 2008]) on UPOL (Dobeš, Machala, Tichavský, & Posp?´šil, 2004) and UBIRIS (Proencca & Alexandre, 2005) iris databases. It is observed that combination of iris color and texture improves the performance of iris recognition system.