Integration of wavelet transform, Local Binary Patterns and moments for content-based image retrieval

Content based image retrieval is grievous need of present scenario in digital imaging world. This work presents a new multi-scale content based image retrieval system which leverages the multi-resolution property of discrete wavelet transform (DWT) and the local information attribute of local extrema patterns (LEPs). Two level DWT is applied on images and wavelet coefficients are obtained for images, further the LEPs are collected from wavelet coefficients to extract the feature vector. The proposed method abbreviated as DWT+LEP and tested on two benchmark databases for validation and compared with local extrema patterns (LEPs), discrete wavelet transform (DWT), center-symmetric local binary pattern (CS LBP), local edge pattern for image retrieval (LEPINV), local edge pattern for segmentation (LEPSEG), local binary pattern (LBP) and block based local binary pattern (BLK LBP).

Content based image retrieval (CBIR) is an extrusive technique of retrieving the relevant images from vast image archives by extracting their low level features. In this research paper, the pursuance of five most prominent texture feature extraction techniques used in CBIR systems are experimentally compared in detail. The main issue with the CBIR systems is the proper selection of techniques for the extraction of low level features which comprises of color, texture and shape. Among these features, texture is one of the most decisive and dominant features. This selection of features completely depends upon the type of images to be retrieved from the database. The texture techniques explored here are Grey level co-occurrence matrix (GLCM), Discrete wavelet transform (DWT), Gabor transform, Curvelet and Local binary pattern (LBP). These are experimented on three touchstone databases which are Wang, Corel-5 K and Corel-10 K. The chief parameters of CBIR systems are evaluated here such as precision, recall and F-measure on all these databases using all the techniques. After detailed investigation it is figured out that LBP, GLCM and DWT provide highlighted and comparable results in all these datasets in terms of average precision. Besides practical implementation, the precised conceptual examination of these three texture techniques is also proposed in this article. So, this analysis is extremely beneficial for selecting the appropriate feature extraction technique by taking into consideration the experimental results along with image conditions such as noise, rotation etc.

In this chapter, a content-based image retrieval technique based on the concept of region-based image retrieval has been described. This technique integrates color, texture, and shape features using local binary patterns (LBPs). In this technique, the image is divided into a fixed number of blocks and from each block LBP-based color, texture, and shape features are computed. The color and texture features are extracted using LBP histograms of quantized color image and gray-level images, respectively. Shape features are computed using the binary edge map obtained using Sobel edge detector from each block. All three features are combined to make a single completed binary region descriptor (CBRD) represented in the LBP way. To support region-based retrieval, an effective region code-based scheme is employed. In this technique, the spatial relative locations of objects are also considered to increase the retrieval accuracy.

This paper presents a novel feature extraction framework for content-based image retrieval (CBIR). Discrete wavelet transform (DWT) based Local tetra pattern (LTrP) is used to obtain the feature map from an input image. Decomposition of DWT up to single level and the features obtained from it would make the CBIR system very sensitive to noise. Therefore, decomposition up to three scales is used to remove noise. On each of the sub band LTrP is applied and 130 features are extracted. Further, Artificial Neural Network (ANN) is employed for index matching and image retrieval task which gives the classification accuracy of 97.9 % for Corel 1K database. We have compared our proposed feature extraction scheme with LTrP and other existing local descriptor. Results show that combination of DWT and Local Tetra Patterns (DWT + LTrP) extracts more robust features than LTrP alone. Also, the effect of different wavelet filters on the accuracy of the system has been analyzed. Proposed framework has been tested on Corel-1000 and Corel-10000 databases. We have used average retrieval rate, precision, and recall for performance measure of the CBIR system. Proposed method outperforms the retrieval rate from 75.9% using LTrP to 97.9% using proposed method on Corel 1K database. Improvement in performance measures as compared to the other existing methods evidence that, the proposed spatio-frequency local descriptor is more robust for image retrieval task.

Nowadays, the majority of images are in JPEG and MPEG compressed formats, and JPEG2000 is considered to be the next generation of compression standard due to the high- performance of discrete wavelet transform (DWT). It is time- consuming and occupies too much memory in conventional image retrieval ways. In order to solve these problems, we use grading retrieval techniques to implement image retrieval based on discrete cosine transform (DCT) compressed domain and DWT compressed domain. For image retrieval based on DCT domain, we use color features: color moment and color histogram, to describe content of images and propose a new dynamic color space quantization based on color distribution; For image retrieval based on DWT domain, we use texture features as two level feature vectors. The mean and standard deviation of low frequency sub-band coefficients are used as the first level retrieval. The means and standard deviations of selected high frequency sub-band coefficients are used as the second level retrieval. Furthermore, the third level retrieval is achieved by the fast wavelet histogram. Our experiment results clearly show that the two grading image retrieval algorithms work better than other algorithms: store memory is reduced and retrieval accuracy is improved. Index Terms—Content based image retrieval (CBIR), compressed domain, discrete cosine transform (DCT), discrete wavelet transform (DWT), color features, texture features

In the field of computer vision and image processing CBIR (Content based image retrieval) system become an active research area. The CBIR technique involves feature extraction based on image features (like color, shape and texture) and defining rules for comparing image. A classifier is also used by authors for image classification. For this purpose an image database taken from James Z. Wang research group website is used by many authors. Due to enormous increase in the size of database traditional methods of image retrieval by using keywords does not provide accurate result. Thus the concept of CBIR system is emerged. In last couple of years many method of feature extraction is developed but since now researcher are not able to develop a system which has gained high accuracy of human visual perception in distinguishing pictures. Therefore earlier researchers have concentrated to increase the retrieval accuracy of CBIR system by using different combination of features. The objective of this paper is to present review on a CBIR technique which combined four kinds of features set including every kind of feature for developing feature vector. Color based features (histogram), statistical features (mean, median and standard deviation), spatial features (Gabor feature and wavelet features) and texture features (GLCM) is used for forming feature vector. A SVM classifier is used with CBIR system to increase retrieval accuracy. SVM (support vector machine) is a machine learning classifier which provides good classification accuracy. Keywords: CBIR, SVM, NN, GLCM, HSV Color space, Euclidean distance matrix.

Medical image indexing and storage is gaining increased importance. Indexing and retrieval of these images efficiently is becomes an essential task. Indexing of medical images using text or numbers is a cumbersome task, difficult to memorize and time consuming. Indexing and Retrieval of images can be done through query by text and query by image which is also known as content based image retrieval (CBIR). Different medical images possess different texture and shape features. This paper presents a novel and hybrid approach of managing a huge medical image database and retrieving a medical image from the database by fusion of shape and texture features. Image is retrieved by comparing the features of query image and images of database. We attach some document information with each medical image such as patient identity, diseases, age, and case history. It can be used for further diagnostic and analysis purpose. Further the proposed algorithm also updates the database automatically if new query is found. MATLAB ® 7.01 and its image processing toolbox have been used to implement the algorithm.

Histogram refinement for texture descriptor based image retrieval

In recent years, the automatic traffic signs recognition(TSR) has attracted researches' attention and it becomes a great challenge in Intelligent Transport System (ITS). Because of complex environmental or weather reasons, it is difficult for TSR to achieve high correct recognition rate and meet real-time at the same time. This paper focus on the traffic signs feature extraction and proposes a new effective approach to recognize the traffic signs with combining local feature and global feature. Center Symmetry Local Binary Pattern (CSLBP) is the improvement of Local Binary Pattern(LBP), We modify the CSLBP with multi sampling as the local feature. The global feature is the low frequency coefficients of Discrete Wavelet Transform (DWT) which owns good multi resolution ability. After extracted the two kinds of feature, we cascade them as the new feature to represent the traffic signs image. The final feature as the input data to classify the traffic signs with Support Vector Machine (SVM). In the GTSRB database, the result shows that the proposed approach achieves a accuracy of 97.67% which is superior to single feature approach and greatly reduces the recognition time.

In content based image retrieval (CBIR) process, every image has been represented in a compact set of local visual features i.e. color, texture, and/or shape of images. This set of local visual features is known as feature vector. In the CBIR process, feature vectors of images have been used to represent or to identify similar images in adequate way. As a result, feature vector construction has always been considered as an important issue since it must reflect proper image semantics using minimal amount of data. The proposed CBIR scheme is based on the combination of color and texture features. In this work initially, we have converted the given RGB image into HSV color image. Subsequently, we have considered H (hue), S (saturation), and V (intensity) components for extraction of visual image features. The texture features have been extracted from the V component of the image using 2D dual-tree complex wavelet transform (2D DT-CWT) where it analyzes the textural patterns in six different directions i.e. ±15\(^{\circ }\), ±45\(^{\circ }\), and ±75\(^{\circ }\). At the same time, we have computed the probability histograms of H and S components of the image respectively and subsequently those are divided into non-uniform bins based on cumulative probability for extraction of color based features. So, in this work both the color and texture features have been extracted simultaneously. Finally, the obtained features have been concatenated to attain the final feature vector and same is considered in image retrieval process. We have tested the novelty and performance of the proposed work in two Corel, two objects, and, a texture image datasets. The experimental results reveal the acceptable retrieval performances for different types of datasets.

As the use of digital media increases, effective retrieval and management techniques become more important. In order to extract useful information from this huge amount of data, many content based image retrieval (CBIR) systems have been developed. A typical CBIR system captures image features such as color, texture, or shape of objects in the query image and tries to retrieve images from the database with similar features. The main advantage of CBIR systems with relevance feedback is that these systems take into account the gap between the high-level concepts and low-level features and subjectivity of human perception of visual content. In this paper, a CBIR system based on color histogram has been presented and a new approach for image storage and retrieval called association-based image retrieval (ABIR) has been studied. The results of both systems have been shown.

Content-based image retrieval (CBIR) has turned into an important and active potential research field with the advance of multimedia and imaging technology. It makes use of image features, such as color, texture and shape, to index images with minimal human intervention. A CBIR system can be used to locate medical images in large databases. In this paper we propose a CBIR system which describes the methodology for retrieving digital human brain magnetic resonance images (MRI) based on textural features and the Adaptive neuro-fuzzy inference system (ANFIS) learning to retrieve similar images from database in two categories: normal and tumoral. A fuzzy classifier has been used, because of the uncertainty in the results of classifier and capacity of learning. ANFIS is a good candidate for our categorization problem. Our proposed CBIR system can locate a query image in the category of normal or tumoral images in the online retrieval part. Finally, using a relevance feedback, we improve the effectiveness of our retrieval system. This research uses the knowledge of the CBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. We present and compare the results of the proposed method with the CBIR systems used in recent works. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency compared with the previous works.

The Aglaonema type and quality is difficult to identify due to leaf pattern variety. For this reason, a technique is developed to classify Aglaonema types from leaf images. The Aglaonema is identified using the Local Binary Pattern (LBP) technique. The LBP recognizes objects in the form of pixel neighbor patterns in binary code, which is sensitive to the radius (R) and the number of neighbors (P) pixels. In this article we will study the appropriate radius and number of neighbors so that the LBP code becomes an accurate abject texture attribute. Experimentally, R is varied from 1 to 5 while P is varied from 4 to 24 pixels. Two types of Aglaonema with two varieties taken from each type were used to test the accuracy of the LBP code. The accuracy of the classification results is carried out with the help of K-Nearest Neighbors (KNN). The results show that the greater the number of neighbors in determining the LBP code, the more accurate the classification results. Neighbors with a total of 18 have a stable accuracy reaching a total of 79%. Increasing the number of neighbors does not significantly affect accuracy. The neighbor radius affects the batik type of Aglaonema, the wider the neighbor area, the accuracy increases up to 84%, but for the Lipstick type, the best accuracy is obtained when R=3. By choosing the right R and P, the types of Aglaonema batik and Lipstick can be differentiated well.

The analysis of image feature robustness using cometcloud

Interpreting medical images is certainly a complex task which requires extensive knowledge [1]. According to Computer Aided Diagnosis (CAD) serves as a second opinion that will help radiologists in diagnosis and on the other hand Content-based Image Retrieval uses visual content to help users browse, search and retrieve similar medical images from a database based on the user’s interest [2-4]. The competency of the CBMIR system depends on feature extraction methods [5]. The textural features are very important to determine the content of a medical image. Textural features provide scenic depth, the spatial distribution of tonal variation, and surface orientation [6]. Therefore, this study seeks to compare and evaluate some of the hand-crafted texture feature extraction techniques in CBMIR. This is to help those concerned in enhancing CBIR systems to make informed decisions concerning the selection of the best textural feature extraction techniques. Since there is no clear indication of which of the various texture feature extraction techniques is best suited for a given performance metric when considering which of the techniques to choose for a particular study in CBMIR systems. The objective of this work, therefore, is to comparatively evaluate the performance of the following texture feature extraction techniques; Local Binary Pattern (LBP), Gabor Filter, Gray-Level Co-occurrence Matrix (GLCM), Haralick Descriptor, Features from Accelerated Segment Test (FAST) and Features from Accelerated Segment Test and Binary Robust Independent Elementary Features (FAST &BRIEF) using the metrics; precision, recall, f1-score, mean squared error (MSE), accuracy and time. These techniques are coupled with specific similarity measure to obtain results. The results showed that LBP, Haralick Descriptor, FAST, and GLCM had the best results in terms of (Precision and Accuracy), Time, F1-Score, and Recall respectively. LBP had 82.05% and 88.23% scores for precision and accuracy respectively. The following scores represent the performance of the Haralick descriptor, FAST, and GLCM models respectively; 0.88s, 38.7%, and 44.82%. These test scores are obtained from datasets ranging from 1k-10.5k. Aside from LBP outperforming the other 5 models mentioned, it still outperformed the following proposed models ' [7]’, ‘Tamura texture feature and wavelet transform combined with Hausdorff distance - [8]’, ‘ [9]’ in terms of (precision, accuracy, and recall) and (precision and recall) respectively and probably f1-score (since f1-score is the weighted average of precision and recall). It is believed that an ensemble of LBP, Haralick descriptors, and Support Vector Machine (SVM) can represent a robust system for both medical image retrieval and classification.