Color Image Retrieval Research Articles

Object Detection in E-Commerce Using YOLO in Real Time

Presently, e-commerce platforms incorporate image search functionalities. Nevertheless, these systems possess constraints; input images necessitate static and manual cropping since the system does not automatically generate bounding boxes. Addressing this concern requires the implementation of an object detection algorithm to ascertain the quantity, location, and type of desired objects within real-time bounding boxes before users finalize their selection. This capability empowers users to readily discern their desired items, thereby augmenting the precision and efficiency of visual searches. Despite the availability of swifter object detection algorithms such as R-CNN and Mask R-CNN, which prioritize accuracy over speed, rendering them less suited for real-time detection, we opted to employ the YOLOv4 algorithm as an alternative, renowned for its efficacy in real-time object detection. Furthermore, we adopted the Color, Texture, and Edge-Based Image Retrieval (CTEBIR) technique for image matching. The results of our experimentation demonstrate that the utilization of the YOLOv4 algorithm can enhance the accuracy and speed of visual searches by streamlining the search process based on the identified classes. Additionally, our precision assessment yielded a score of 95%, with individual scores for camera objects reaching 90%, keyboards achieving 85%, and laptops attaining 71%. These findings corroborate the dependability of the CTEBIR algorithm in image matching and contribute to a deeper comprehension of the system's efficacy in accurately detecting and distinguishing objects.

IMAGING ANALYSIS IN AGRICULTURAL LAND SURFACE CHARACTERIZATION - CASE STUDY ON WHEAT STUBBLE

The study used imaging analysis (UAV images) to analyze and characterize an agricultural land area resulting from the harvest of the wheat crop. The researches were carried out in the framework of ARDS Lovrin, agricultural year 2021-2022. The agricultural land was worked with a disk, and the vegetable remains (straw, stubble) were partially incorporated into the soil. The images were taken with the drone (DJI Phantom 4) at variable heights, between H = 0.5 m and H = 50 m from the ground level (19 series of images from variable heights). The analysis of the images resulted in values in the RGB color system. Normalized values (rgb) and NDI and INT indices were calculated. The color parameter R (red) varied between R = 77.36 – 89.14±0.71, the color parameter G (green) varied between G = 71.29 – 81.99±0.60, and the color parameter B (blue) varied between B = 71.42 – 79.29±0.47. The values of the calculated indices varied between NDI = 0.040 – 0.048±0.0007, respectively INT = 73.357 – 83.217±0.582. Correlations of varying levels of intensity were recorded between color parameters, calculated indies and image retrieval height. High variability was recorded in the case of the NDI index (CV = 7.3156), and low value in the case of the normalized value g (CV = 0.4443). Polynomial equations described the variation of some color parameters in relation to the image retrieval height (p<0.001). The variation of the NDI index in relation to the image acquisition height was described by a spline model, under statistical safety conditions ( ). Associated with the image retrieval height, the NDI index presented four ranges of values, respectively uniform values on the 0.5 - 1 m height interval, and on the 25 - 50 m height interval; variation of 0.001 units on the height interval 2 - 8 m, and high variability on the height interval 9 - 20 m.

Colour Image Retrieval Based on Adaptive Jseg Image Segmentation and Statistical Distance Measure

Colour Image Retrieval Based on Adaptive Jseg Image Segmentation and Statistical Distance Measure

A novel color image retrieval method based on texture and deep features

A novel color image retrieval method based on texture and deep features

Bionic Birdlike Imaging Using a Multi-Hyperuniform LED Array.

Digital cameras obtain color information of the scene using a chromatic filter, usually a Bayer filter, overlaid on a pixelated detector. However, the periodic arrangement of both the filter array and the detector array introduces frequency aliasing in sampling and color misregistration during demosaicking process which causes degradation of image quality. Inspired by the biological structure of the avian retinas, we developed a chromatic LED array which has a geometric arrangement of multi-hyperuniformity, which exhibits an irregularity on small-length scales but a quasi-uniformity on large scales, to suppress frequency aliasing and color misregistration in full color image retrieval. Experiments were performed with a single-pixel imaging system using the multi-hyperuniform chromatic LED array to provide structured illumination, and 208 fps frame rate was achieved at 32 × 32 pixel resolution. By comparing the experimental results with the images captured with a conventional digital camera, it has been demonstrated that the proposed imaging system forms images with less chromatic moiré patterns and color misregistration artifacts. The concept proposed verified here could provide insights for the design and the manufacturing of future bionic imaging sensors.

Mean distance local binary pattern: a novel technique for color and texture image retrieval for liver ultrasound images

A rapid growth in medical ultrasound database makes it difficult for medical practitioners to manage and search relevant data with good efficiency. Hence, a novel image retrieval technique using Mean Distance Local Binary Pattern (Mean Distance LBP) has been proposed for content-based image retrieval. The conventional local binary pattern (LBP) converts every pixel of image into a binary pattern based on their relationship with neighbourhood pixels. The proposed feature descriptor differs from local binary pattern as it transforms the mutual relationship of all neighbouring pixels in a binary pattern based on their standard deviation templates as well as Euclidean distance from the center pixel. Color feature and Gray Level Co-occurrence Matrix have also been used in this work. To prove the excellence of the proposed method, experiments have been conducted on two different databases of natural images and face images. Further, the method is applied on real time ultrasound database for retrieval of liver images from a set of ultrasound images of various organs. The performance has been observed using well-known evaluation measures, precision and recall, and compared with some state-of-art local patterns. Comparison shows a significant improvement in the proposed method over existing methods.

Maximal multi-channel local binary pattern with colour information for CBIR

Content Based Image Retrieval (CBIR) focuses on retrieving images from repositories based on visual features extracted from the images. Texture and colour are one of the popularly used feature combination in CBIR. A major challenge in colour image retrieval is the characterization of features of the constituent channels and their integration. The commonly adopted methodology include extraction of features of various channels followed by their concatenation. However, the resulting image feature vector is generally of high dimensionality. To address this problem, in this paper a texture-colour descriptor is proposed integrating the multi-channel features. For texture computation, a fixed sized local intensity based descriptor, Maximal Multi-channel Local Binary Pattern (MMLBP), which integrates the multi-channel local binary information through an adder-map followed by thresholding is introduced. The histogram of the obtained patterns is used for representing the image texture. Colour information is captured by quantizing the RGB colour space and is represented with histogram. The colour-texture descriptors are further fused to characterize the images. The efficacy of the descriptor is evaluated by carrying out retrieval on benchmarked datasets for image retrieval such as Wang’s 1 K, Corel 5 K, Corel 10 K, Coloured Brodatz Texture and Zubud, using precision and recall measures as evaluation metrics. It is observed that the proposed descriptor presents improved retrieval performance over the databases under consideration and outperforms other descriptors.

Colour fast‐match for precise vehicle retrieval

The explosive growth of vehicles has increased the importance of intelligent traffic system. However, compared with face recognition, vehicle retrieval has not attracted the attention of researchers in vision community. Precise vehicle retrieval has always been a challenging task because it requires the retrieval of all vehicles with the same visual attributes from a large number of vehicles with subtle visual differences. To handle this, the authors propose to implement precise vehicle retrieval using an improved fast affine matching colour image retrieval method based on the features of annual inspection label area. Moreover, regional colour constant and hue and saturation feature are introduced to the proposed method so as to settle the illumination change problem in the real surveillance scene. To fully evaluate the proposed algorithm, they perform experiments on the ReIDcar and VehicleID datasets, which differ in data scale and image quality. The experimental results show that the presented algorithm outperforms traditional methods in vehicle retrieval. It is verified that the extracted features and the feature matching method can distinguish subtle differences between vehicles.

An Improved Image Retrieval by Using Texture Color Descriptor with Novel Local Textural Patterns

This paper proposes a new local descriptor of color, texture known as a Median Binary Pattern for color images (MBPC) and Median Binary Pattern of the Hue (MBPH). These suggested methods are extract discriminative features for the color image retrieval. In the surrounding region of a local window, the suggested descriptor classification uses a plane to a threshold that distinguish two classes of color pixels. The Median Binary Patterns of the hue features are derived in the color space from HIS, called MBPH to maximize the discriminatory power of the proposed MBPC operator. In addition to MBPC, MBPH are fused to extract the MBPC+MBPH resulting in an efficient image recovery method combined with color histogram (CH). The structure of the two suggested MBPC and MBPH descriptors are combined with the other fuzzyfied based color histogram descriptor that formed MBPC+MBPH+FCH to improve the performance of the suggested method. The proposed methods are applied on datasets Wang, Corel-5K, and Corel-10K. Experimental results depicted that results of proposed methods are better than existing method in terms of retrieved accuracy. The significant recognition accuracy obtained from the proposed methods which is 60.1 and 63.9 for Wang dataset, 41.88 and 42.47 for Corel-5K and 32.89 and 33.89 for Corel-10K dataset. This hybrid proposed method greatly deals with different textural patterns as well as able to grasp minute color details.

Statistical tree-based feature vector for content-based image retrieval

The efficiency of any content-based image retrieval system depends on the extracted feature vectors of individual images stored in the database. Generation of a compact feature vector with good discriminative power is a real challenge in the image retrieval system. This paper presents the experimentation carried out to generate compact feature vectors for a colour image retrieval system based on image content. It has two stages of operation. In first stage the energy compaction property of image transforms is used whereas in the second stage, the statistical tree approach is used for feature vector generation. Performance of image retrieval is tested using image feature database as per various performance evaluation parameters such as precision recall crossover point (PRCP) along with newly proposed conflicting string of images (CSI). With different colour spaces, image transforms and statistical measures, proposed approach achieves the reduction in the feature vector size with better discriminative power.

A novel feature descriptor for image retrieval by combining modified color histogram and diagonally symmetric co-occurrence texture pattern

In this paper, we have proposed a novel feature descriptors combining color and texture information collectively. In our proposed color descriptor component, the inter-channel relationship between Hue (H) and Saturation (S) channels in the HSV color space has been explored which was not done earlier. We have quantized the H channel into a number of bins and performed the voting with saturation values and vice versa by following a principle similar to that of the HOG descriptor, where orientation of the gradient is quantized into a certain number of bins and voting is done with gradient magnitude. This helps us to study the nature of variation of saturation with variation in Hue and nature of variation of Hue with the variation in saturation. The texture component of our descriptor considers the co-occurrence relationship between the pixels symmetric about both the diagonals of a 3 × 3 window. Our work is inspired from the work done by Dubey et al. (IEEE Signal Process Lett 22(9):1215–1219, [2015]). These two components, viz. color and texture information individually perform better than existing texture and color descriptors. Moreover, when concatenated the proposed descriptors provide a significant improvement over existing descriptors for content base color image retrieval. The proposed descriptor has been tested for image retrieval on five databases, including texture image databases—MIT-VisTex database and Salzburg texture database and natural scene databases Corel 1K, Corel 5K and Corel 10K. The precision and recall values experimented on these databases are compared with some state-of-art local patterns. The proposed method provided satisfactory results from the experiments.

Color image storage and retrieval using quantum mechanics

In this paper, two algorithms for storing and retrieving a color image using quantum mechanics are proposed. The first algorithm encodes a pixel’s color information with an angle. In this case, it has some advantages in retrieval speed, required qubits and quantum measurements. In the second algorithm, a pixel’s color is divided into three channels: R, G and B, and each channel is encoded with an angle. In this case, it has an advantage in retrieval accuracy. By example analysis, the proposed algorithms are compared with the other algorithms in some aspects, e.g., the total number of qubits, the total number of measurements and the accuracy of retrieval. It can be found that the first algorithm has the advantage in the speed of retrieval, the total number of qubits and the total number of measurements, and the second algorithm has the advantage in the accuracy of retrieval.

A novel adaptive LBP-based descriptor for color image retrieval

In this paper, we present two approaches to extract discriminative features for color image retrieval. The proposed local texture descriptors, based on Radial Mean Local Binary Pattern (RMLBP), are called Color RMCLBP (CRMCLBP) and Prototype Data Model (PDM). RMLBP is a robust to noise descriptor which has been proposed to extract texture features of gray scale images for texture classification.For the first descriptor, the Radial Mean Completed Local Binary Pattern is applied to channels of the color space, independently. Then, the final descriptor is achieved by concatenating the histogram of the CRMCLBP_S/M/C component of each channel. Moreover, to enhance the performance of the proposed method, the Particle Swarm Optimization (PSO) algorithm is used for feature weighting.The second proposed descriptor, PDM, uses the three outputs of CRMCLBP (CRMCLBP_S, CRMCLBP_M, CRMCLBP_C) as discriminative features for each pixel of a color image. Then, a set of representative feature vectors are selected from each image by applying k-means clustering algorithm. This set of selected prototypes are compared by means of a new similarity measure to find the most relevant images. Finally, the weighted versions of PDM is constructed using PSO algorithm.Our proposed methods are tested on Wang, Corel-5k, Corel-10k and Holidays datasets. The results show that our proposed methods makes an admissible tradeoff between speed and retrieval accuracy. The first descriptor enhances the state-of-the-art color texture descriptors in both aspects. The second one is a very fast retrieval algorithm which extracts discriminative features.

Creating Color Image Signature Based On Laplacian Equation

Colour image retrieval, identification and recognition play an important role in vital computerized applications, so creating an image signature which can be used as a key to identify the image is an essential and important problem to be solved. In this paper a new method of image signature extraction will be proposed. The proposed method will be implemented and tested and the experimental results will be compared with other method results to prove the efficiency of the proposed method by mean of creating a unique signature which will require a minimum memory space and a smaller time to extract the signature.

Color Image Retrieval Based on Quaternion and Deep Features

In this paper, aiming at the inefficient question of feature extraction and multi-feature fusion in existing algorithms, we propose a color image retrieval algorithm based on quaternion and deep features. Firstly, we replace the real part of the quaternion with the image information entropy to form a new image matrix, and extracting its moment invariant as the traditional features. Meanwhile, the deep features are extracted by the improved convolutional neural network. Secondly, we develop an effective features fusion procedure. Finally, retrieval results are obtained by Euclidean distance between image’s fusion features. In order to demonstrate the effectiveness of our method, we have conducted extensive experiments on three well known datasets (INRIA Holidays dataset, University of Kentucky Benchmark, Oxford 5k).

An Efficient System for Color Image Retrieval Representing Semantic Information to Enhance Performance by Optimizing Feature Extraction

Image retrieval is a well discussed field in digital image processing. Images can be sort out from a big collection and database of images on the basis of text, color and shape of objects in images. In CBIR systems, using combined features get many most similar and relevant images. In a typical CBIR system, the ocular content of the images from the large collection is extracted and displayed by a storehouse like m-dimensional feature vectors framed out from images. This vector of the images in the collection of images in database is named as a database of features. Most sought out systems represent images with color feature most related to user and shape feature to relate image to exact object from collection and test dataset. In this paper we present introduction, research work on color scheme with HSV colour space and its combination with shape using coiflet wavelet methods. States of art design of overall system is delineated with results evaluated for mean average precision and mean average recall for overall system.

Statistical tree-based feature vector for content-based image retrieval

The efficiency of any content-based image retrieval system depends on the extracted feature vectors of individual images stored in the database. Generation of a compact feature vector with good discriminative power is a real challenge in the image retrieval system. This paper presents the experimentation carried out to generate compact feature vectors for a colour image retrieval system based on image content. It has two stages of operation. In first stage the energy compaction property of image transforms is used whereas in the second stage, the statistical tree approach is used for feature vector generation. Performance of image retrieval is tested using image feature database as per various performance evaluation parameters such as precision recall crossover point (PRCP) along with newly proposed conflicting string of images (CSI). With different colour spaces, image transforms and statistical measures, proposed approach achieves the reduction in the feature vector size with better discriminative power.

Content-Based Color Image Retrieval Using Block Truncation Coding Based on Binary Ant Colony Optimization

In this paper, we propose a content-based image retrieval (CBIR) approach using color and texture features extracted from block truncation coding based on binary ant colony optimization (BACOBTC). First, we present a near-optimized common bitmap scheme for BTC. Then, we convert the image to two color quantizers and a bitmap image-utilizing BACOBTC. Subsequently, the color and texture features, i.e., the color histogram feature (CHF) and the bit pattern histogram feature (BHF) are extracted to measure the similarity between a query image and the target image in the database and retrieve the desired image. The performance of the proposed approach was compared with several former image-retrieval schemes. The results were evaluated in terms of Precision-Recall and Average Retrieval Rate, and they showed that our approach outperformed the referenced approaches.

Color Image Retrieval Based on Chernoff Distance Measure

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Creating Color Image Features Using Local Contrast Method

Digital color images are now one of the most popular data types used in the digital processing environment. Color image recognition plays an important role in many vital applications, which makes the enhancement of image recognition or retrieval system an important issue. Using color image pixels to recognize or retrieve the image, but the issue of the huge color image size that requires accordingly more time and memory space to perform color image recognition and/or retrieval. In the current study, image local contrast was used to create local contrast victor, which was then used as a key to recognize or retrieve the image. The proposed local contrast method was properly implemented and tested. The obtained results proved its efficiency as compared with other methods.