Color Layout Descriptor Research Articles

A Secure Image Outsourcing Using Privacy-Preserved Local Color Layout Descriptor in Cloud Environment

A Secure Image Outsourcing Using Privacy-Preserved Local Color Layout Descriptor in Cloud Environment

Efficient Shot Boundary Detection with Multiple Visual Representations

Due to the unlimited growth of video-capturing devices and media, searching and finding a particular video in this huge database becomes a laborious as well as expensive task. Information-rich shots are the inevitable factor of the content-based video processing (CBVP) system. Hence, shot boundary detection (SBD) becomes the basic step of all content-based video retrieval processes. The accuracy of the existing SBD methods highly suffers from false positives and false negatives due to the presence of multiple variants. An efficient SBD method with multiple invariant features is proposed in this paper. A right combination of invariant features such as edge change ratio (ECR), colour layout descriptor (CLD), and scale-invariant feature transform (SIFT) key point descriptors helped to improve the accuracy level of SBD. As the selected features are invariant to most of the variants in video frames, such as illuminance changes, motion, scaling, and rotation, a markable reduction in false detection is possible. Support vector machine (SVM) classifier is used for the classification of frames into transition frames and shot frames. This proposed method is experimented and analysed with the standard SBD dataset TRECVid 2007 videos. The experimental results are compared with some state-of-art methods, and our method shows better performance with a 97% of F1 score.

A pattern recognition method on smartphones for planar chromatography and verification on chromatograms of four herbal medicines from citrus fruits

Image retrieval techniques in combination with support vector machine (SVM) on smartphones were studied to make pattern recognition of planar chromatograms. A micellar TLC method employed less toxic and flammable solvents was studied to discriminate four herbal medicines (HMs) from citrus fruits. And the chromatograms were used to test the pattern recognition method. The TLC method employed O/W microemulsion–formic acid (98:2) as the mobile phase, polyamide as the solid phase, and 1% aluminum trichloride aqueous solution as the visualization reagent. TLC of samples of dried lemon peel, dried sweet-orange peel, pummelo peel, and mandarin orange peel of varying origins were performed in three separate runs. Image retrieval techniques combined with SVM directly classified the chromatograms based on migration path images. A mobile application running on an Android smartphone was developed to perform the pattern recognition. The micellar TLC method satisfactorily characterized the chemical profile of the four HMs from citrus fruits. The pattern recognition method combining color layout descriptor with SVM correctly classified all the chromatograms. A smartphone with an application combining color layout descriptor with SVM would be a convenient tool for pattern recognition of planar chromatograms.

Computer-aided diagnosis based on hand thermal, RGB images, and grip force using artificial intelligence as screening tool for rheumatoid arthritis in women.

Rheumatoid arthritis (RA) is an autoimmune disorder that typically affects people between 23 and 60years old causing chronic synovial inflammation, symmetrical polyarthritis, destruction of large and small joints, and chronic disability. Clinical diagnosis of RA is stablished by current ACR-EULAR criteria, and it is crucial for starting conventional therapy in order to minimize damage progression. The 2010 ACR-EULAR criteria include the presence of swollen joints, elevated levels of rheumatoid factor or anti-citrullinated protein antibodies (ACPA), elevated acute phase reactant, and duration of symptoms. In this paper, a computer-aided system for helping in the RA diagnosis, based on quantitative and easy-to-acquire variables, is presented. The participants in this study were all female, grouped into two classes: class I, patients diagnosed with RA (n = 100), and class II corresponding to controls without RA (n = 100). The novel approach is constituted by the acquisition of thermal and RGB images, recording their hand grip strength or gripping force. The weight, height, and age were also obtained from all participants. The color layout descriptors (CLD) were obtained from each image for having a compact representation. After, a wrapper forward selection method in a range of classification algorithms included in WEKA was performed. In the feature selection process, variables such as hand images, grip force, and age were found relevant, whereas weight and height did not provide important information to the classification. Our system obtains an AUC ROC curve greater than 0.94 for both thermal and RGB images using the RandomForest classifier. Thirty-eight subjects were considered for an external test in order to evaluate and validate the model implementation. In this test, an accuracy of 94.7% was obtained using RGB images; the confusion matrix revealed our system provides a correct diagnosis for all participants and failed in only two of them (5.3%). Graphical abstract.

A Novel Method for Feature Extraction using Color Layout Descriptor (CLD) and Edge histogram Descriptor (EHD)

Now days, Image processing finds diversified applications in almost all field of life. The success of any image processing application is depends on proper feature extraction technique. To extract good and proper features is very interesting and challenging task in the development process. It is used to describe the image based on its contents. These extracted features are used to compare, analyse and/or search the analogous images. There are various feature extraction techniques are found in the literature to design various applications. However any image processing application generates images with high dimensionality, which will be results in the low efficiency of an application. This paper provides an approach to extract features from the images using MPEG-7 feature extraction techniques. The approach discussed in the paper uses two popular MPEG-7 visual content descriptors; they are namely Edge Histogram Descriptor (EHD) and Color Layout Descriptor (CLD). The concept results in reduction of dimensions of an image to improve the efficiency of the application. It can be used as a heart to design any image processing application as well as provides strong foundation to develop variety of applications.

Classification of yield affecting biotic and abiotic paddy crop stresses using field images

On-time recognition and early control of the stresses in the paddy crops at the booting growth stage is the key to prevent qualitative and quantitative loss of agricultural yield. The conventional paddy crop stress identification and classification activities invariably rely on human experts to identify visual symptoms as a means of categorization. This process is admittedly subjective and error-prone, which in turn may lead to incorrect action in stress management decisions. The proposed work presented in this paper aims to develop an automated computer vision system for the recognition and classification of paddy crop stress types from the field images using the state-of-the-art color features. The work examines the impact of eleven stress types, two biotic and nine abiotic stresses, on five different paddy crop varieties during the booting growth stage using field images and analyzes the stress responses in terms of color variations using lower-order color moments and two visual color descriptors defined by the MPEG-7 standard, the Dominant Color Descriptor (DCD) and Color Layout Descriptor (CLD). The Sequential Forward Floating Selection (SFFS) algorithm has been employed to reduce the overlapping between the features. Three different classifiers, the Back Propagation Neural Network (BPNN), the Support Vector Machine (SVM), and the k-Nearest Neighbor (k-NN) have been deployed to distinguish among stress types. The average stress classification accuracies of 89.12%, 84.44% and 76.34% have been achieved using the BPNN, SVM, and k-NN classifiers, respectively. The proposed work finds application in the development of decision support systems and mobile apps for the automation of crop and resource management practices in the field of agricultural science.

Automatic Scale Severity Assessment Method in Psoriasis Skin Images Using Local Descriptors.

Psoriasis is a chronic skin condition. Its clinical assessment involves four measures: erythema, scales, induration, and area. In this paper, we introduce a scale severity scoring framework for two-dimensional psoriasis skin images. Specifically, we leverage the bag-of-visual words (BoVWs) model for lesion feature extraction using superpixels as key points. BoVWs model is based on building a vocabulary with specific number of words (i.e., codebook size) by using a clustering algorithm with some local features extracted from a constructed set of key points. This is followed by three-class machine learning classifiers for scale scoring using support vector machine (SVM) and random forest. Besides, we examine eight different local color and texture descriptors, namely color histogram, local binary patterns, edge histogram descriptor, color layout descriptor, scalable color descriptor, color and edge directivity descriptor (CEDD), fuzzy color and texture histogram, and brightness and texture directionality histogram. Further, the selection of codebook and superpixel sizes are studied intensively. A psoriasis image set, consisting of 96 images, is used in this study. The conducted experiments show that color descriptors have the highest performance measures for scale severity scoring. This is followed by the combined color and texture descriptors, whereas texture-based descriptors come last. Moreover, K-means algorithm shows better results in vocabulary building than Gaussian mixed model, in terms of accuracy and computations time. Finally, the proposed method yields a scale severity scoring accuracy of 80.81% using the following setup: a superpixel of size [Formula: see text], a combined color and texture descriptor (i.e., CEDD), a constructed codebook of size 128 using K-means, and SVM for scale scoring.

Abnormal event detection and localization using level set based on hybrid features

In this paper, a new method using level set is proposed for abnormal event detection and localization in video surveillance. From an input video, five image descriptors, namely the color moments, the edge histogram descriptors, the color and edge directivity descriptors, the color layout descriptors, and the scalable color descriptors, are extracted for robust detection. We employ the local binary fitting model as the statistical learning model to update by the input videos in real time. We performed experiments on the publicly available UCSD anomaly detection dataset and showed that our method has good performance for detecting and localizing abnormality compared to the state-of-the-art methods.

GEO matching regions: multiple regions of interests using content based image retrieval based on relative locations

Information retrieval systems are getting more attention in the era of multimedia technologies such as an image, video, audio and text files. The large numbers of images are challenges in computer systems field to store, manage data effectively and efficiently. The shape retrieval feature of different objects in the image also remains a difficult problem due to distinct angle view of different objects in a scene only; few studies have reported solution to the problem of finding relative locations of ROIs. In this paper, we proposed three methods such as1. Geolocation-based image retrieval (GLBIR), 2.Unsupervised feature technique Principal component analysis (PCA) and 3.multiple region-based image retrieval. The first proposed (GLBIR) method identifies geo location an image using visual attention based mechanism and its color layout descriptors. These features are extracted from geo-location of query image from Flickr database. Our proposed model does not fully semantic understanding of image content, uses visual metrics for example; the proximity ,color contrast, size and nearness to image's boundaries to locate viewer's attention. We analyzed results and compared with state of art CBIR Systems and GLBIR Technique. Our second method to refine images exploiting and fusing by unsupervised feature technique using principal component analysis (PCA). The visually similar images clustering together with analyses image retrieval process and remove outliers initially retrieved image set by PCA. To evaluation our proposed approach, we used thousands of images downloaded from Flickr and CIFAR-10 databases using Flickr public API. Finally, we determinately proposed a system for image retrieval based on region. It provides a user interface for availing to designate the watershed ROI within an input image. During the retrieval of images, regions' feature vectors having codes of region homogeneous to a region of input image are utilized for comparison. Standard datasets are used for evaluation of proposed approach. The experiment demonstrates and effectiveness of the proposed method to achieve higher annotation performance increases accuracy and reduces image retrieval time. We evaluated our proposed approach on images dataset from Flickr and CIFAR-10.

A New Efficient Optimal 2D Views Selection Method Based on Pivot Selection Techniques for 3D Indexing and Retrieval

In this paper, we propose a new method for 2D/3D object indexing and retrieval. The principle consists of an automatic selection of optimal views by using an incremental algorithm based on pivot selection techniques for proximity searching in metric spaces. The selected views are afterward described by four well-established descriptors from the MPEG-7 standard, namely: the color structure descriptor (CSD), the scalable color descriptor (SCD), the edge histogram descriptor (EHD) and the color layout descriptor (CLD). We present our results on two databases: The Amsterdam Library of Images (ALOI-1000), consisting of 72,000 color images of views, and the Columbia Object Image Library (COIL-100), consisting of 7200 color images of views. The results prove the performance of the developed method and its superiority over the k-means algorithm and the automatic selection of optimal views proposed by Mokhtarian et al.

Multi-channel descriptors and ensemble of Extreme Learning Machines for classification of remote sensing images

We present a method for real-time scene classification which achieves high accuracy without a time consuming descriptor learning step and kernelized classifiers. Robustness of the classification is achieved by combining the powerful multi-channel Gabor-based descriptors and an ensemble of Extreme Learning Machines (ELM). We first extend the recently introduced Binary Gabor Patterns (BGP) to multi-channel images. This is done by extracting BGP over several color channels and embedding an additional compact color layout descriptor. Then we propose an effective method for the aggregation of multiple ELMs into a single classification system, which leads to significant classification accuracy improvements. The experimental evaluation demonstrates that multi-channel color information constantly improves classification results. The integration of multiple ELMs into an ensemble using the proposed aggregation strategy significantly outperforms linear SVM in terms of accuracy, and reaches results similar to the non-linear SVM while operating in real time. Therefore, an ensemble of ELMs with the proposed aggregation strategy could be used as an efficient alternative to the non-linear SVM for remote sensing image classification tasks.

Localizing global descriptors for content-based image retrieval

In this paper, we explore, extend and simplify the localization of the description ability of the well-established MPEG-7 (Scalable Colour Descriptor (SCD), Colour Layout Descriptor (CLD) and Edge Histogram Descriptor (EHD)) and MPEG-7-like (Color and Edge Directivity Descriptor (CEDD)) global descriptors, which we call the SIMPLE family of descriptors. Sixteen novel descriptors are introduced that utilize four different sampling strategies for the extraction of image patches to be used as points of interest. Designing with focused attention for content-based image retrieval tasks, we investigate, analyse and propose the preferred process for the definition of the parameters involved (point detection, description, codebook sizes and descriptors’ weighting strategies). The experimental results conducted on four different image collections reveal an astonishing boost in the retrieval performance of the proposed descriptors compared to their performance in their original global form. Furthermore, they manage to outperform common SIFT- and SURF-based approaches while they perform comparably, if not better, against recent state-of-the-art methods that base their success on much more complex data manipulation.

Use of whole genome DNA spectrograms in bacterial classification

A spectrogram reflects the arrangement of nucleotides through the whole chromosome or genome. Our previous study suggested that the spectrogram of whole genome DNA sequences is a suitable tool for the determination of relationships among bacteria. Related bacteria have similar spectrograms, and similarity in spectrograms was measured using a color layout descriptor. Several parameters, such as the mapping of four bases into a spectrogram, the number of considered elements in the color layout descriptor, the color model of the image and the building tree method, can be changed. This study addresses the use of parameter selection to ensure the best classification results. The quality of the classification was measured by Matthew’s correlation coefficient (MCC). The proposed method with optimal parameters (called SpectCMP—Spectrogram CoMParison method) achieved an average MCC of 0.73 at the phylum level. The SpectCMP method was also tested at the order level; the average MCC in the classification of class Gammaproteobacteria was 0.76. The success of a classification with respect to the correct phyla was compared to three methods that are used in bacterial phylogeny: the CVTree method, OGTree method and moment vector method. The results show that the SpectCMP method can be used in bacterial classification at various taxonomic levels.

Significant region-based image retrieval

In the era of multimedia technologies, the need for information/data retrieval systems getting more attention. The data might be image, video, audio and/or text files. Digital libraries, surveillance application, web applications and many other applications that handle huge volume of data essentially have data retrieval components. In this paper, a technique of region-based image retrieval (RBIR), a branch of content-based image retrieval (CBIR), is proposed. The proposed model identifies a significant region in an image using visual attention-based mechanism and represents them using its color layout descriptors and curvelet descriptors. These features are extracted from significant region of query image and images in the database. The likeness between the query region and database image region is ranked according to a similarity measure computed from the feature vectors. The proposed model does not need a full semantic understanding of image content, uses visual metrics such as proximity, size, color contrast and nearness to image’s boundaries to locate viewer’s attention and uses curvelet transform in combination with color layout descriptor to represent the significant region. Experimental results are analyzed and compared with the state-of-the-art CBIR Systems and RBIR Technique.

VIDEO COPY DETECTION USING SEGMENTATION METHOD AND MPEG-7 DESCRIPTORS

There are a number of methods available for video copy detection. Some of the methods were employing the application of local and global descriptors which were found to be ineffective in detections involving complex transformations. In order to overcome the above specified inefficiency, Scale Invariant Feature Transform (SIFT) descriptor came into picture but was found to have a high computational cost. The method proposed in this paper involving five different types of MPEG-7 descriptors namely Color and Edge Directivity Descriptor (CEDD), Fuzzy Color and Texture Histogram (FCTH), Scalable Color Descriptor (SCD), Edge Histogram Descriptor (EHD), Color Layout Descriptor (CLD) for extracting the features of the frames in the selected video is found to be cost effective and efficient even in case of high level of transformations. This paper also throws light on certain improvements in graphbased video sequence matching method which is used to overcome the level of noise, to detect videos with different frame rates and optimal sequence matching is found automatically from the disordered video sequences by applying spatial features during copy detection. Experimental results have showed that the proposed method is far effective than the previously existing video detection scenarios.

INFORMATION SEARCH USING TEXT AND IMAGE QUERY

An image retrieval and re-ranking system utilizing a visual re-ranking framework which is proposed in this paper the system retrieves a dataset from the World Wide Web based on textual query submitted by the user. These results are kept as data set for information retrieval. This dataset is then re-ranked using a v isual query (multiple images selected by user from the dataset) which conveys user’s intention semantically. Visual descriptors (MPEG-7) which describe image with respect to low-level fea ture like color, texture, etc are used for calculating distances. These distances are a measure of similarity between query images and m embers of the dataset. Our proposed system has been assessed on different type s of queries such as apples, Console, Paris, etc. Isignificant improvement on initial text-based search results.This system is well suitable for online shopping application. Index Terms : MPEG-7, Color Layout Descriptor (CLD), Edge Histo gram Descriptor (EHD), image retrieval and reranking system

Colour and texture feature‐based image retrieval by using Hadamard matrix in discrete wavelet transform

Image retrieval is one of the most applicable image processing techniques, which has been used extensively. Feature extraction is one of the most important procedures used for interpretation and indexing images in content-based image retrieval systems. Effective storage, indexing and managing a large number of image collections is a critical challenge in computer systems. There are many proposed methods to overcome these problems. However, the rate of accurate image retrieval and speed of retrieval is still an interesting field of research. In this study, the authors propose a new method based on combination of Hadamard matrix and discrete wavelet transform (HDWT) in hue-min-max-difference colour space. An average normalised rank and combination of precision and recall are considered as metrics to evaluate and compare the proposed method against different methods. The obtained results show that the use of HDWT provides better performance in comparison with Haar discrete wavelet transform, colour layout descriptor, dominant colour descriptor and scalable colour descriptor, Padua point and histogram intersection.

The colour and texture - a novel image retrieval technology based on human vision

If we want the computer to behave like humans, especially when they are recognising an image, we must study the human vision system carefully. Obviously, the colour and the texture are the most important perceptual characteristics of humans. When someone retrieval an image, he or she can use a variant of colour spaces, which have different advantages. In this paper, we replace the original colour space with HSV in MPEG-7 colour layout descriptor according to the nature of the human vision system. Experiment shows that the modified method increased retrieval efficiency greatly. Further more, we establish a multi-feature space, both homogeneous texture descriptor and colour layout descriptor are used. Since there are differences in human perceptions of colour and texture, in order to successfully retrieval an image which caters to the users, parallel genetic algorithm PGA is employed to adjust the weight of each feature space. The experimental evidence shows that the method made the computer behave like human.

Image retrieval based on the combination of RGB and HSV's histograms and Colour Layout Descriptor

In this paper we present the first stage of a new approach to improve the precision and recall of the content-based image retrieval task. To do this, we aim to combine three colour features, RGB and HSV histograms, and MPEG- 7 Colour Layout Descriptor. To perform the combination, we propose to use an approximation based on Borda Voting-Schemes. Under that the Borda Voting-Schemes needs at least three votes to perform the combination, we intend to use the K-Nearest Neighbors methods to select the candidate images, given a query image. In the second stage, we’ll implement our approach using at least three image databases.

基于三像素彩色共生矩阵的图像检索Content-Based Image Retrieval Using Three Pixels Color Co-Occurrence Matrix

本文提出一种新颖的颜色特征描述子——三像素彩色共生矩阵。它通过描述三个相同像素的空间相关性来实现基于内容的图像检索。它首先将彩色图像从RGB颜色空间转换到Lab均匀颜色空间，然后将彩色图像量化为90种颜色，并且对图像进行共生矩阵分析，最后采用90维特征向量来描述图像特征。实验结果表明，三像素彩色共生矩阵的检索性能优越于灰度共生矩阵和MPEG-7颜色布局描述子。它能够描述图像的颜色结构分布信息，能够整合颜色和纹理特征。 A new color descriptor, namely three pixels color co-occurrence matrix, is proposed in this paper. It imple- ments content-based image retrieval via representing the spatial correlation of the same three pixels. The proposed de- scriptor first converts color image from RGB color space to Lab color space. The original color image is then quantized into 90 colors and co-occurrence matrix used for image analysis. Finally, 90-dimensional vector is adopted to describe image features. Image retrieval experimental results shown that the performances of the proposed descriptor are better than that of gray level co-occurrence matrix and MPEG-7 color layout descriptor. It has good discriminative power of color structure distribution, and can combine the color and texture features.