Content-based Image Indexing Research Articles

An efficient content-based medical image indexing and retrieval using local texture feature descriptors

This paper presents an efficient medical image indexing and retrieval method using two new proposed feature descriptors named as threshold local binary AND pattern (TLBAP) and local adjacent neighborhood average difference pattern (LANADP). In basic local binary pattern (LBP), every center pixel is considered as a threshold to generate the binary pattern, whereas in the proposed method a threshold value is calculated using the highest pixel intensity of the neighboring pixels to construct the threshold local binary pattern (TLBP). Thereafter, logical AND operation is performed between LBP and TLBP pattern to produce TLBAP pattern. The objective of the other feature descriptor named here as LANADP is to explore the relationship of neighboring pixels with its adjacent neighbors in vertical, horizontal and diagonal directions. In the proposed work, both TLBAP and LANADP features are concatenated in the form of the histograms to generate the final features vector and the performance of the system is evaluated. To test the effectiveness of the proposed method, three publicly available medical image databases, namely OASIS-MRI brain images, NEMA-CT images and VIA/ELCAP-CT images, are used. Two measures, viz. average retrieval precision and average retrieval rate, have been used to evaluate the performance of the method proposed which is further compared with some existing local pattern-based methods. The experimental results show that the proposed methods give better results as compared to the other existing methods considered in this study.

Locally Aggregated Hierarchical Decomposition Based Ensemble Learning for Robust Face Recognition

For the detection and extraction of features, a number of hierarchical layering methods have been explored extensively. This is followed by content-based image indexing and retrieval implemented by taking local aggregation of the extracted features. Through a clustering process, a mapping between the numerous patches in the image to the centres of the learned clusters is made. A histogram representation reflecting each of the independent features together form the vectorized encoded image. A global feature vector is then obtained. A comparative study is made with the traditional feature extraction and aggregation techniques following which we extensively explore various learning algorithms to enhance the stability and performance. In preference to using the existing hypothesis space from which the learning models are derived from, we make use of a hybridized hypothesis space which is a combination of the existing hypothesis spaces to get an enhanced hypothesis set. The key idea behind fusing multiple models is to decrease bias which arises from the spurious assumptions that might be concluded by the model, decrease the variance owing to the sensitivity due to minor aberrations that creep in the training phase and to improve predictions. We extend this concept of ensemble system by combining conceptually dissimilar base classifiers and use the majority vote for final prediction. This is performed to balance out limitations of the individual classifiers. A five-fold cross-validation technique was employed on the standard datasets like Grimace and Faces96. Our proposed model can achieve the best accuracy of 99.7 percent on the Grimace dataset and constantly hit in excess of 95 percent for far more complex datasets which are a direct implication of the high tolerance to variances in scale, rotation, pose and expression.

Color Facial Image Representation with New Quaternion Gradients

This paper proposes a new color image representation and multiple feature fusion based method for improving color face recognition performance under different lighting conditions. First, a new image color image representation has been derived. Second, a quaternion gradient has been given to enhance and extract the faces/object's edges, contours, and texture. Also, we propose a novel feature representation based on Quaternion Gradient-based LBP tool for color face recognition. Finally, we present a concept of combining the color facial recognition system, which is based on the local quaternion gradients based binary patterns LBP Image Representation, and a new color-to-gray new mapping. The presented concept can be used for surveillance, security systems, computer animation, face tagging, human–computer interface, biometric identification, behavioral analysis, content-based image and video indexing applications.

A novel local octa-pattern feature descriptor for image retrieval

The local tetra patterns (LTrPs) gives four-directional information and ignores the diagonal pixel information, thereby affecting the retrieved image efficiency. In the present work, a novel retrieval approach has been proposed using local octa-patterns (LOcPs) for content-based image indexing and retrieval. The proposed approach encodes the center pixel directional information with its eight adjacent neighbors, from the directions that are computed using the first-order derivatives. Also the nth-order LOcP is computed using \((n-1)\)th-order local direction variations. In addition, the performance of the developed method by combining it with the Gabor transform has been analyzed. The performance of the proposed technique has been compared to existing techniques like LBP, LTP, LDP, and LTrP on Corel-1000 database (DB1) and Describable Textures Dataset (DB2). The performance observed shows that the developed method improves the retrieval parameters from 75.9%/77.13% to 79.4%/81.5% in the form of average precision on DB1/DB2 databases.

Ekstraksi Fitur Warna, Tekstur dan Bentuk untuk Clustered-Based Retrieval of Images (CLUE)

Picture (image) is a media that used for storing visual data, for example, two-dimensional images are often used to store an incident. Images on the internet media growth very rapidly. There are a lot of image, video, text or other content on the Internet. Image Index and image retrieval again become a topic of research in the last decade in which concentrated on how to get the meaning of an information contained in an image. Three methods outlined in the search for an image, the text-based image retrieval, content-based image retrieval and indexing images in the order of language. This study focuses on the preparation of the features of an image based on color and texture. Features colors using the average value of Hue image, texture features using Gray Level occurance Matrix (GLCM). Color, texture, and shape extraction technique resulted in eighteen (18) feature that can be used as features in the process of Clustering.DOI: 10.24843/MITE.1601.12

Performance Enhancement of Patch-based Descriptors for Image Copy Detection

Images have become main sources for the informa-tion, learning, and entertainment, but due to the advancement and progress in multimedia technologies, millions of images are shared on Internet daily which can be easily duplicated and redistributed. Distribution of these duplicated and transformed images cause a lot of problems and challenges such as piracy, redundancy, and content-based image indexing and retrieval. To address these problems, copy detection system based on local features are widely used. Initially, keypoints are detected and represented by some robust descriptors. The descriptors are computed over the affine patches around the keypoints, these patches should be repeatable under photometric and geometric transformations. However, there exist two main challenges with patch based descriptors, (1) the affine patch over the keypoint can produce similar descriptors under entirely different scene or the context which causes “ambiguity”, and (2) the descriptors are not enough “distinctive” under image noise. Due to these limitations, the copy detection systems suffer in performance. We present a framework that makes descriptor more distinguishable and robust by influencing them with the texture and gradients in vicinity. The experimental evaluation on keypoints matching and image copy detection under severe transformations shows the effectiveness of the proposed framework.

Errata [Challenges in content-based image indexing of cultural heritage collections

Errata [Challenges in content-based image indexing of cultural heritage collections

Signal Processing for Art Investigation [From the Guest Editors

While early research in applying signal processing to art investigation emphasized image acquisition and image manipulation, various manifestations of image feature mining have achieved more prominence in the past decade as evidenced by the preceding citations providing successive indicators of the directions being taken in this growing field. The 11 articles in this special issue of SPM include studies of photographic paper classification, ancient coin classification, Mayan epigraphy analysis, 3-D color print graph signal processing, laid paper chain-line pattern matching, content-based image indexing, canvas weave analysis, crack detection for simulated in-painting, painting style characterization, and face recognition in portraits. All of these articles engage, to a substantial degree, in image feature mining. There is an enormous need in art history and conservation to locate, classify, identify, and measure features in multispectral and multidirectional images of artworks. One barrier of entry into this field is access to images of sufficient quality and quantity of artworks. Therefore, for this special issue, the authors were requested to make the images they processed accessible to other researchers (when possible) to stimulate the exploration of new and improved solutions, and most have complied. With that added bonus, we welcome you to the modest beginnings of an emerging field that promises to offer many satisfying challenges and will require specialization of and advances in the tools and techniques of signal processing.

Challenges in Content-Based Image Indexing of Cultural Heritage Collections: Support vector machine active learning with applications to text classification

Cultural heritage collections are being digitized and made available through online tools. Due to the large volume of documents being digitized, not enough manpower is available to provide useful annotations. Is this article, we discuss the use of automatic tools to both automatically index the documents (i.e., provide labels) and search through the collections. We detail the challenges specific to these collections as well as research directions that must be followed to answer the questions raised by these new data.

Gaussian DBC Co-Occurrence Matrix for Content-Based Image Indexing and Retrieval

Gaussian DBC Co-Occurrence Matrix for Content-Based Image Indexing and Retrieval

Integration of Color and Local Derivative Pattern Features for Content-Based Image Indexing and Retrieval

This paper presents two new feature descriptors for content based image retrieval (CBIR) application. The proposed two descriptors are named as color local derivative patterns (CLDP) and inter color local derivative pattern (ICLDP). In order to reduce the computational complexity the uniform patterns are applied to both CLDP and ICLDP. Further, uniform CLDP (CLDPu2) and uniform ICLDP (ICLDPu2) are generated respectively. The proposed descriptors are able to exploit individual (R, G and B) spectral channel information and co-relating pair (RG, GB, BR, etc.) of spectral channel information. The retrieval performances of the proposed descriptors (CLDP and ICLDP) are tested by conducting two experiments on Corel-5000 and Corel-10000 benchmark databases. The results after investigation show a significant improvement in terms of precision, average retrieval precision (ARP), recall and average retrieval rate (ARR) as compared to local binary patterns (LBP), local derivative patterns (LDP) and other state-of-the-art techniques for image retrieval.

Content based image indexing and retrieval using directional local extrema and magnitude patterns

In this paper, we integrate the concept of directional local extremas and their magnitude based patterns for content based image indexing and retrieval. The standard ditectional local extrama pattern (DLEP) extracts the directional edge information based on local extrema in 0°, 45°, 90°, and 135° directions in an image. However, they are not considering the magnitudes of local extremas. The proposed method integrates these two concepts for better retrieval performance. The sign DLEP (SDLEP) operator is a generalized DLEP operator and magnitude DLEP (MDLEP) operator is calculated using magnitudes of local extremas. The performance of the proposed method is compared with DLEP, local binary patterns (LBPs), block-based LBP (BLK_LBP), center-symmetric local binary pattern (CS-LBP), local edge patterns for segmentation (LEPSEG) and local edge patterns for image retrieval (LEPINV) methods by conducting two experiments on benchmark databases, viz. Corel-5K and Corel-10K databases. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to other existing methods on respective databases.

Content-based Image Retrieval using Visual Attention Point Features

One of the challenges in the development of a content-based image indexing and retrieval application is to achieve an efficient and robust indexing scheme. Color is a fundamental image feature used in content-based image retrieval (CBIR) systems. This paper proposes a robust and effective image retrieval scheme, which is based on the weighed color histogram of visual attention points. Firstly, the fully affine invariant visual attention points are extracted from the origin color image by using the Affine-SIFT (scale-invariant feature transform) detector. Secondly, according to the color complexity measure (CCM) theory, the visual weight values for the significant visual attention points are calculated to reflect the image local variation. Then, the weighed color histogram of visual attention points is constructed. Finally, the similarity between color images is computed by using the weighed color histogram of visual attention points. Experimental results show that the proposed image retrieval is not only more accurate and efficient in retrieving the user-interested images, but also yields higher retrieval accuracy than some state-of-the-art image retrieval schemes for various test DBs.

An incremental evolutionary learning method for optimizing content-based image indexing algorithms

One of the future directions of content-based image retrieval (CBIR) systems is incremental learning of indexing and retrieval algorithms. Optimization of the indexing algorithm is more difficult compared to the retrieval algorithm enhancement; since each time the indexing algorithm parameters are modified, all images of the reference database should be indexed again. This paper considers, for the first time, a challengeable limitation of actual indexing optimization systems: learning in dynamic and incremental CBIR environments. We introduce a new incremental evolutionary optimization method based on evolutionary group algorithm. The new incremental evolutionary group algorithm (IEGA) overcomes time-consuming drawbacks related to general evolutionary algorithms in large scale content-based image indexing optimization tasks and presents a new strategy that is enhanced with the ability of incremental learning. Evaluation results on some simulated dynamic CBIR systems show that the proposed method can continuously obtain good performance in the presence of environmental or scale changes.

Searching of Images Based on Content Using Blobs

Now a day’s image searching is still a challenging problem in content based image retrieval (CBIR) system. Most system operates on all images without pre-sorting the images. The image search result contains many unrelated image. The aim of this research is to propose a new method for content based image indexing and research based on blobs feature extraction and existing edges in the image and classification of image to different type and to search image which are similar the given research.

System profiles in content-based image indexing and retrieval

In this paper, a novel study on system profiles and adaptation of parameters for end-users of content-based indexing and retrieval (CBIR) applications are presented. The main objective of the study is improving the overall CBIR application performance in different hardware platforms having different technical capabilities and conditions. We define CBIR system profiles in terms of hardware and system platform attributes and propose CBIR parameters for each profile. Hence, the study consists of two main parts: system profiling and adaptation of indexing and retrieval parameters for each profile. The proposed CBIR parameters are appropriate configurations for optimal CBIR use on every platform. The proposed parameters for each system profile are assessed over a large set of experiments. Experimental studies show that the proposed parameters for each system profile have satisfactory semantic retrieval performance, with reduced computational complexity and storage space requirement. 45 to 78% improvement is achieved in the computational complexity of the retrieval process depending on the profile.

Feature selection for content-based image retrieval

In this article, we propose a novel system for feature selection, which is one of the key problems in content-based image indexing and retrieval as well as various other research fields such as pattern classification and genomic data analysis. The proposed system aims at enhancing semantic image retrieval results, decreasing retrieval process complexity, and improving the overall system usability for end-users of multimedia search engines. Three feature selection criteria and a decision method construct the feature selection system. Two novel feature selection criteria based on inner-cluster and intercluster relations are proposed in the article. A majority voting-based method is adapted for efficient selection of features and feature combinations. The performance of the proposed criteria is assessed over a large image database and a number of features, and is compared against competing techniques from the literature. Experiments show that the proposed feature selection system improves semantic performance results in image retrieval systems.

Image retrieval: Benchmarking visual information indexing and retrieval systems

Image retrieval: Benchmarking visual information indexing and retrieval systems

A Novel Evolutionary Approach for Optimizing Content-Based Image Indexing Algorithms

Optimization of content-based image indexing and retrieval (CBIR) algorithms is a complicated and time-consuming task since each time a parameter of the indexing algorithm is changed, all images in the database should be indexed again. In this paper, a novel evolutionary method called evolutionary group algorithm (EGA) is proposed for complicated time-consuming optimization problems such as finding optimal parameters of content-based image indexing algorithms. In the new evolutionary algorithm, the image database is partitioned into several smaller subsets, and each subset is used by an updating process as training patterns for each chromosome during evolution. This is in contrast to genetic algorithms that use the whole database as training patterns for evolution. Additionally, for each chromosome, a parameter called age is defined that implies the progress of the updating process. Similarly, the genes of the proposed chromosomes are divided into two categories: evolutionary genes that participate to evolution and history genes that save previous states of the updating process. Furthermore, a new fitness function is defined which evaluates the fitness of the chromosomes of the current population with different ages in each generation. We used EGA to optimize the quantization thresholds of the wavelet-correlogram algorithm for CBIR. The optimal quantization thresholds computed by EGA improved significantly all the evaluation measures including average precision, average weighted precision, average recall, and average rank for the wavelet-correlogram method.

Speeding up the similarity search in high-dimensional image database by multiscale filtering and dynamic programming

This paper presents a scalable content-based image indexing and retrieval system based on a new multiscale filter. Image databases often represent the image objects as high-dimensional feature vectors and access them via the feature vectors and similarity measure. A similarity measure based on the proposed multiscale filtering technique is defined to reduce the computational complexity of the similarity search in high-dimensional image database. Moreover, a special attention is paid to solve the problem of feature value correlation by dynamic programming. This problem arises from changes of images due to database updating or considering spatial layout in constructing feature vectors. The computational complexity of similarity measure in high-dimensional image database is very huge and the applications of image retrieval are restricted to certain areas. To demonstrate the effectiveness of the proposed algorithm, we conducted extensive experiments and compared the performance with the IBM's query by image content (QBIC) and Jain and Vailaya's methods. The experimental results demonstrate that the proposed method outperforms both of the methods in retrieval accuracy and noise immunity. The execution speed of the proposed method is much faster than that of QBIC method and it can achieve good results in terms of retrieval accuracy compared with Jain's method and QBIC method.