Color Structure Descriptor Research Articles

Notice of Violation of IEEE Publication Principles: An assessment of MPEG-7 visual descriptors for images of maize plagues and diseases

Notice of Violation of IEEE Publication Principles <br><br>“An Assessment of MPEG-7 Visual Descriptors for Images of Maize Plagues and Diseases” <br> by J. Manjarrez-Sanchez <br> in the IEEE Latin America Transactions, Vol. 18, No. 8, August 2020 <br><br> After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. <br><br> This paper contains significant portions of original content from the paper cited below. The original content was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. <br><br> “Análisis de descriptores visuales MPEG-7 para enfermedades y plagas en plantas de maíz” <br> by Luis Angel Pinedo Sánchez <br> in his Bachelor’s Thesis, Instituto Technologico Superior de Jerez <br><br> <br/> Feature description is a fundamental process in the analysis of images for content-based retrieval and classification, among other tasks in which the image feature descriptor should have enough discriminative power to differentiate similar from dissimilar images according with a distance measure. Although several descriptors have been proposed for a variety of images, the challenge is their suitability to solve these tasks efficiently. One proposal is the set of standard MPEG-7 visual descriptors. We address their suitability to efficiently describe plagues and diseases in images of maize plants. The importance of this crop is its worldwide relevance for human and animal consumption. Experiments for similarity search queries using a set of distance measures, show that the Color Structure Descriptor with the Bray-Curtis distance is the most efficient and provides around 68% precision in most cases.

Analysis of Color Standardization Methods for the Automatic Quantification of IHQ Stain in Breast TMA

Introduction/ Background IHC biomarkers in breast TMA samples are used daily in pathology departments. This generates large amounts of information, which requires careful analysis [1]. Automatic methods to evaluate positive staining have been investigated since they may save time and reduce errors in the diagnosis that are due to subjective evaluation. Aims The aim of this work is to develop a density tool able to automatically quantify the positive brown IHC stain in breast TMA. One of the problems when dealing with stained samples is color variation and distortion [2]. This is due to several factors such as the fixation process, the amount of stain or the digitalization process. One solution to the color variation problem is to apply standardization of reagents and procedures in histological practice. However, stains fade over time and therefore, it is not possible to achieve complete standardization with the current technology. In this paper, different methods for stain normalization have been analyzed and compared in density quantification. Methods The methods implemented for stain normalization are based on the use of color distribution by means of dominant color descriptor, scalable and color structure descriptor. These algorithms adjust the color values of an image on a pixel-by-pixel basis so as to match the color distribution of the source image to that of a target image. Then, two main processes were performed to estimate TMA density: a) evaluation of total cylinder area and b) quantification of IHC stained area. For the 1st process, the algorithm distinguishes between normal, broken or distorted cylinders. The 2nd process deals with the evaluation of the positive brown pixels inside the cylinder. The segmentation is based on Lab thersholding together with binary thresholding applied to the H, S and B channels of the HSV and RGB color models. Finally, the tool segments all the positive areas and quantifies the brown density areas. Results A dataset of 879 TMA images were used to evaluate the methods. TMAs were prepared with an automatic tissue arrayer (Arraymold tool) composed of 50 holes/TMA with a cylinder diameter of 2mm. Slides were stained using different IHC stains, that is, CD1A, CD4, CD8, CD21, CD57, CD68, CD83, CD123, CK19, OXP3, LAMp3 and S100. The acquisition of the digital TMA images was done with Aperio ScanScope XT scanner at 40x (0.25 µm/ pixel). Afterwards, each cylinder image was individually extracted [3]. The use of color standardization makes the segmentation robust and free of parameter setting. Furthermore, the standardization process is able to reduce noise and facilitate the density quantification. The results were compared to manual density quantification by expert pathologists. The tests carried out provided up to 98% agreement when color standardization was applied against 90% without color standardization. The biggest error comes from FOXP3 samples.

GLOBAL AND LOCAL DESCRIPTOR FOR CBIR AND IMAGE ENHANCEMENT USING MULTI-FEATURE FUSION METHOD

The research is ongoing in CBIR it is getting much popular. In this retrieval of image is done using a technique that searches the necessary features of image. The main work of CBIR is to get retrieve efficient, perfect and fast results.In this algorithm, fused multi-feature for color, texture and figure features. A global and local descriptor (GLD) is proposed in this paper, called Global Correlation Descriptor (GCD) and Discrete Wavelet Transform (DWT), to excerpt color and surface feature respectively so that these features have the same effect in CBIR. In addition, Global Correlation Vector (GCV) and Directional Global Correlation Vector (DGCV) is proposed in this paper which can integrate the advantages of histogram statistics and Color Structure Descriptor (CSD) to characterize color and consistency features respectively. Also, this paper is implemented by Hu moment (HM) for shape feature, it extract 8 moments for image. For the classification process, apply kernel Support vector machine (SVM). The experimental result has computed precision, recall, f_measure and execution time. Also, worked on two datasets: Corel-1000 and Soccer-280.

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.

Adequation and hardware implementation of the color structure descriptor for real-time temporal video segmentation

In this paper, we focused on the study of the MPEG-7 color structure descriptor (CSD) for real-time temporal video segmentation. A new hardware architecture of the CSD was proposed. In the aim of optimizing this implementation in terms of hardware resources and execution time, different algorithm transformations have been tested for the considered application. The CSD was applied for different quantization levels in the HMMD color space and for different grey levels, with and without a frame skip. We have demonstrated that the use of a low number of quantization levels and a frame skip can significantly reduce the complexity and assure a better computing performance while preserving a satisfactory level of accuracy in terms of shot boundary detection rate. This is useful for implementation on resource-constrained hardware platform and multiprocessing applications. The performances of the proposed architecture were evaluated for different quantization levels to show the effect on occupied hardware resources and execution time. The comparative study demonstrates the effectiveness of the proposed architecture, which can operate in real-time video and without restriction on image size. In this work we have also designed a system on chip (SOC) for real-time video summarization based on the CSD. The proposed SOC integrating the CSD module was developed using a platform based on a Xilinx Virtex5 FPGA. A complete demonstration, including CSD extraction, shot boundary detection and key-frames visualization, was realized.

Efficient Content based Image Retrieval System using Mpeg-7 Features

This paper gives an overview idea of efficient retrieval of images using different Mpeg-7 Features. Content Based Image Retrieval is a technique of automatic indexing and retrieving of images from a large data base. Feature Extraction and Similarity Matching are the two major steps for CBIR Systems. Color, Texture and Shape represent the three visual features for any image. Mpeg-7 Stands for Multimedia Content Description Interface. The main objective of Mpeg-7 is to provide a standardized set of technologies for describing multimedia content. It has allowed quick and efficient content identification, and addressing a large range of applications. The visual descriptors are classified according to the feature such as color, shape, texture, etc. This paper has used Color Structure Descriptor for color and Edge Histogram Descriptor for texture.These two features are also integrated to increase the performance of CBIR Systems. The efficiency of all methods are demonstrated with the help of results.

Design of Video Retrieval System Using MPEG-7 Descriptors

The paper proposes a content-based video retrieval system designed using MPEG-7 (multimedia content description interface), which provides a standard description for a video. The system consists of three parts: shot boundary detection, feature extraction and similarity measurement. In shot boundary detection, cut and dissolve can be detected using the histogram difference and skipping image difference, respectively. In feature extraction part, two MPEG-7 visual descriptors, Color Structure Descriptor (CSD) and Edge Histogram Descriptor (EHD), are used to represent the color feature and edge feature of the key frames. Lastly, the similarity between key frames is calculated using dynamic-weighted feature similarity calculation. The proposed system is tested on three kinds of videos. Promising results are obtained in terms of both effectiveness and efficiency.

Embedded colour image coding for content-based retrieval

We present an image coding method explicitly designed for easy content access, i.e., content-based image retrieval. Based on a number of well-studied conventional image coding methods, namely segmentation-based image coding (SBIC), vector quantization (VQ), and a recently developed coloured pattern appearance model (CPAM), we have developed an image coding method with a compressed stream from which effective image content descriptors can be derived with very little computation. A colour image is first segmented adaptively into homogeneous regions of various sizes. Each region is then decomposed into three channels according to the CPAM and VQ is employed to represent the chromatic and achromatic spatial patterns efficiently. The image content descriptors are the joint probability distributions of the segmented region sizes and their achromatic and chromatic spatial patterns’ VQ codebook indices. From image indexing and content-based retrieval perspective, this work can be regarded as a method effectively exploiting/employing image coding technologies to develop novel and effective image descriptors for content-based image retrieval. We have applied the newly developed image content descriptor to content-based image retrieval from a large colour photo image database. Experimental results demonstrate that the new method is comparable to state of the art methods, such as colour correlogram and the latest MPEG7 colour structure descriptor in content-based image retrieval.