Color And Edge Directivity Descriptor Research Articles

A Comparative Study of Image Descriptors in Recognizing Human Faces Supported by Distributed Platforms

Face recognition is one of the emergent technologies that has been used in many applications. It is a process of labeling pictures, especially those with human faces. One of the critical applications of face recognition is security monitoring, where captured images are compared to thousands, or even millions, of stored images. The problem occurs when different types of noise manipulate the captured images. This paper contributes to the body of knowledge by proposing an innovative framework for face recognition based on various descriptors, including the following: Color and Edge Directivity Descriptor (CEDD), Fuzzy Color and Texture Histogram Descriptor (FCTH), Color Histogram, Color Layout, Edge Histogram, Gabor, Hashing CEDD, Joint Composite Descriptor (JCD), Joint Histogram, Luminance Layout, Opponent Histogram, Pyramid of Gradient Histograms Descriptor (PHOG), Tamura. The proposed framework considers image set indexing and retrieval phases with multi-feature descriptors. The examined dataset contains 23,707 images of different genders and ages, ranging from 1 to 116 years old. The framework is extensively examined with different image filters such as random noise, rotation, cropping, glow, inversion, and grayscale. The indexer’s performance is measured based on a distributed environment based on sample size and multiprocessors as well as multithreads. Moreover, image retrieval performance is measured using three criteria: rank, score, and accuracy. The implemented framework was able to recognize the manipulated images using different descriptors with a high accuracy rate. The proposed innovative framework proves that image descriptors could be efficient in face recognition even with noise added to the images based on the outcomes. The concluded results are as follows: (a) the Edge Histogram could be best used with glow, gray, and inverted images; (b) the FCTH, Color Histogram, Color Layout, and Joint Histogram could be best used with cropped images; and (c) the CEDD could be best used with random noise and rotated images.

Privacy-Preserving Content-Based Image Retrieval Using Compressible Encrypted Images

In this article, we propose a novel content-based image-retrieval (CBIR) scheme using compressible encrypted images called “encryption-then-compression (EtC) images.” The proposed scheme allows us not only to directly retrieve images from visually protected images but to also apply EtC images that can be compressed by using the JPEG standard for the first time. In addition, the sensitive management of secret keys is not required in our framework. The proposed retrieval scheme is carried out on the basis of weighted searching images with MPEG-7-powered localized descriptors (weighted SIMPLE descriptors) combining scalable color descriptor (SCD) or color and edge directivity descriptor (CEDD). Weighted SIMPLE descriptors are extended, and CEDD is also modified to avoid the influence of image encryption. In an experiment, the proposed scheme is demonstrated to have almost no degradation in retrieval performance compared with conventional content-based retrieval methods with plain images under the use of two datasets. In addition, the proposed scheme is shown to outperform conventional privacy-preserving CBIR schemes including state-of-the-art ones in terms of mean average precision (mAP) scores.

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.

MDCBIR-MF: multimedia data for content-based image retrieval by using multiple features

Due to recent development in technology, the complexity of multimedia is significantly increased and the retrieval of similar multimedia content is an open research problem. In the service of multimedia service, the requirement of Multimedia Indexing Technology is increasing to retrieve and search for interesting data from huge Internet. Since the traditional retrieval method, which is using textual index, has limitation to handle the multimedia data in current Internet, alternatively, the more efficient representation method is needed. Content-Based Image Retrieval (CBIR) is a process that provides a framework for image search and low-level visual features are commonly used to retrieve the images from the image database. The basic requirement in any image retrieval process is to sort the images with a close similarity in term of visual appearance. The color, shape, and texture are the examples of low-level image features. The feature combination that is also known as feature fusion is applied in CBIR to increase the performance, a single feature is not robust to the transformations that are in the image datasets. This paper represents a new Content-Based Image Retrieval (CBIR) technique to fuse the color and texture features to extract local features as our feature vector. The features are created for each image and stored as a feature vector in the database. The proposed research is divided into three phases that feature extraction, similarities match, and performance evaluation. Color Moments (CM) are used for Color features and extract the Texture features, used Gabor Wavelet and Discrete Wavelet transform. To enhance the power of feature vector representation, Color and Edge Directivity Descriptor (CEDD) is also included in the feature vector. We selected this combination, as these features are reported intuitive, compact and robust for image representation. We evaluated the performance of our proposed research by using the Corel, Corel-1500, and Ground Truth (GT) images dataset. The average precision and recall measures are used to evaluate the performance of the proposed research. The proposed approach is efficient in term of feature extraction and the efficiency and effectiveness of the proposed research outperform the existing research in term of average precision and recall values.

A LoCATe‐based visual place recognition system for mobile robotics and GPGPUs

SummaryIn this paper, a novel visual Place Recognition approach is evaluated based on a visual vocabulary of the Color and Edge Directivity Descriptor (CEDD) to address the loop closure detection task. Even though CEDD was initially designed so as to globally describe the color and texture information of an input image addressing Image Indexing and Retrieval tasks, its scalability on characterizing single feature points has already been proven. Thus, instead of using CEDD as a global descriptor, we adopt a bottom‐up approach and use its localized version, Local Color And Texture dEscriptor, as an input to a state‐of‐the‐art visual Place Recognition technique based on Visual Word Vectors. Also, we use a parallel execution pipeline based on a previous work of ours using the well established General Purpose Graphics Processing Unit (GPGPU) computing. Our experiments show that the usage of CEDD as a local descriptor produces high accuracy visual Place Recognition results, while the parallelization used allows for a real‐time implementation even in the case of a low‐cost mobile device.

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.

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.