Local and deep texture features for classification of natural and biomedical images

Developing efficient feature descriptors is very important in many computer vision applications including biomedical image analysis. In the past two decades and before the popularity of deep learning approaches in image classification, texture features proved to be very effective to capture the gradient variation in the image. Following the success of the Local Binary Pattern (LBP) descriptor, many variations of this descriptor were introduced to further improve the ability of obtaining good classification results. However, the problem of image classification gets more complicated when the number of images increases as well as the number of classes. In this case, more robust approaches must be used to address this problem. In this thesis, we address the problem of analyzing biomedical images by using a combination of local and deep features. First, we propose a novel descriptor that is based on the motif Peano scan concept called Joint Motif Labels (JML). After that, we combine the features extracted from the JML descriptor with two other descriptors called Rotation Invariant Co-occurrence among Local Binary Patterns (RIC-LBP) and Joint Adaptive Medina Binary Patterns (JAMBP). In addition, we construct another descriptor called Motif Patterns encoded by RIC-LBP and use it in our classification framework. We enrich the performance of our framework by combining these local descriptors with features extracted from a pre-trained deep network called VGG-19. Hence, the 4096 features of the Fully Connected 'fc7' layer are extracted and combined with the proposed local descriptors. Finally, we show that Random Forests (RF) classifier can be used to obtain superior performance in the field of biomedical image analysis. Testing was performed on two standard biomedical datasets and another three standard texture datasets. Results show that our framework can beat state-of-the-art accuracy on the biomedical image analysis and the combination of local features produce promising results on the standard texture datasets.Includes bibliographical reference

Implementation of GLCM for Features Extraction and Selection of Batik Images

Batik is a craft that has high artistic value and has been a part of Indonesian culture (especially Java) for a long time. Batik cloth in Indonesia has various types of batik textures, batik cloth colors, and batik fabric patterns that reflect the regional origins of the batik cloth. Regarding the image of batik, the texture feature is an important feature because the ornaments on the batik cloth can be seen as different texture compositions. Besides batik motifs, also influenced by the shape characteristics that become parts of each batik motif. This research will add insight and knowledge to understand batik patterns based on the characteristics of batik motifs, namely texture. There are five batik motifs used, namely inland solo batik, semarang coastal batik, sidhomukti batik, parangklithik batik, and sidhodrajat batik. Initially preprocessing is done by cropping and grayscalling. Of the five image motifs, a cropping process is carried out for each motif. The next step is feature extraction. The features of GLCM were selected in this study. From the features contained in the GLCM, in this study four features were chosen, namely contrast, energy, correlation, and homogeneity. The final step is the selection or selection of features. The result of the feature selection of the four features carried out feature extraction are energy and homogeneity

EKSTRAKSI FITUR BERBASIS WAVELET PADA SISTEM TEMU KEMBALI CITRA TEKSTUR

Pada penelitian ini diimplementasikan suatu sistem temu kembali citra tekstur dengan ekstraksi fitur berbasis wavelet. Ekstraksi fitur terdiri dari 2 proses utama, yaitu dekomposisi wavelet terhadap data citra, dan perhitungan energi dan deviasi standar terhadap koefisien-koefisien wavelet hasil dekomposisi. Penelitian ini bertujuan untuk mengetahui performance sistem temu kembali citra tekstur dengan menggunakan beberapa vektor fitur yang diekstrak dari beberapa level dekomposisi wavelet yang berbeda. Untuk uji coba, dibuat database citra menggunakan citra tekstur Brodatz. Filter yang digunakan pada proses dekomposisi diturunkan dari fungsi wavelet Daubechies4, dan untuk perhitungan kemiripan antara citra contoh dan citra dalam database digunakan Canberra distance. Hasil ujicoba menunjukkan bahwa performance terbaik dari sistem temu kembali citra tekstur adalah recall sebesar 92,5%, yang didapat dengan menggunakan vektor fitur yang diekstrak dari hasil dekomposisi wavelet sampai dengan level 2. Kata kunci: Temu kembali citra tekstur,Ttransformasi wavelet, Canberra distanc

A fast image retrieval method designed for network big data

In the field of big data applications, image information is widely used. The value density of information utilization in big data is very low, and how to extract useful information quickly is very important. So we should transform the unstructured image data source into a form that can be analyzed. In this paper, we proposed a fast image retrieval method which designed for big data. First of all, the feature extraction method is necessary and the feature vectors can be obtained for every image. Then, it is the most important step for us to encode the image feature vectors and make them into database, which can optimize the feature structure. Finally, the corresponding similarity matching is used to determined the retrieval results. There are three main contributions for image retrieval in this paper. New feature extraction method, reasonable elements ranking and appropriate distance metric can improve the algorithm performance. Experiments show that our method has a great improvement in the effective performance of feature extraction and can also get better search matching results

دراسة مقارنة لخوارزميات الأنماط المحلية في أنظمة استرجاع الصور المعتمدة على المحتوى

إن الحاجة المتزايدة لاسترجاع الصور من قواعد البيانات الضخمة جعلت مجال استرجاع الصور بالاعتماد على المحتوى Content-Based Image Retrieval (CBIR) مجالاً ملحاً وضرورياً للبحث. اقترح الباحثون الكثير من خوارزميات استرجاع الصور من خلال استخراج السمات الهامة والمميزة من المحتوى المرئي للصورة لأهمية السمات المستخرجة في تحسين دقة أنظمة الاسترجاع. وفي هذا البحث، تم إجراء مقارنة لست خوارزميات محلية مشهورة على مدى عقد من الزمن (LBP, LTP, LTrP, MMCM, COALTP and LMP)  واختبار هذه الخوارزميات باستخدام نوعين مختلفين من قواعد البيانات: قواعد بيانات الصور الملونة(Color image database) و قواعد بيانات النسجة(Texture database) وباستخدام أربعة مقاييس للمسافات(L1, Euclidean, Cityblock and Cosine)  لاسترجاع الصور الأكثر مطابقة لصورة الاستعلام من خلال اختيار الصور ذات المسافة الأقصر. تم تقييم أداء الخوارزميات المدروسة باستخدام ثلاثة مقاييس: متوسط ​​دقة الاسترجاع (Average Retrieval Precision) ARP، متوسط الاسترداد Average Recall  ومتوسط ​​معدل الاسترجاع (Average Retrieval Rate) ARR، كشفت هذه الدراسة تفوق خوارزمية COALTP على الخوارزميات الأخرى المختبرة، وبالإضافة إلى ذلك، أظهرت النتائج أن خوارزميات الأنماط المحلية أكثر كفاءة في استرجاع الصور من قواعد بيانات النسجة مقارنة مع قواعد البيانات الملونة

Analysis of textural image features for content based retrieval

Digital archaelogy and virtual reality with archaeological artefacts have been quite hot research topics in the last years 55,56 . This thesis is a preperation study to build the background knowledge required for the research projects, which aim to computerize the reconstruction of the archaelogical data like pots, marbles or mosaic pieces by shape and ex ural features. Digitalization of the cultural heritage may shorten the reconstruction time which takes tens of years currently 61 ; it will improve the reconstruction robustness by incorporating with the literally available machine vision algorithms and experiences from remote experts working on a no-cost virtual object together. Digitalization can also ease the exhibition of the results for regular people, by multiuser media applications like internet based virtual museums or virtual tours. And finally, it will make possible to archive values with their original texture and shapes for long years far away from the physical risks that the artefacts currently face. On the literature 1,2,3,5,8,11,14,15,16 , texture analysis techniques have been throughly studied and implemented for the purpose of defect analysis purposes by image processing and machine vision scientists. In the last years, these algorithms have been started to be used for similarity analysis of content based image retrieval 1,4,10 . For retrieval systems, the concurrent problems seem to be building efficient and fast systems, therefore, robust image features haven't been focused enough yet. This document is the first performance review of the texture algorithms developed for retrieval and defect analysis together. The results and experiences gained during the thesis study will be used to support the studies aiming to solve the 2D puzzle problem using textural continuity methods on archaelogical artifects, Appendix A for more detail. The first chapter is devoted to learn how the medicine and psychology try to explain the solutions of similiarity and continuity analysis, which our biological model, the human vision, accomplishes daily. In the second chapter, content based image retrieval systems, their performance criterias, similiarity distance metrics and the systems available have been summarized. For the thesis work, a rich texture database has been built, including over 1000 images in total. For the ease of the users, a GUI and a platform that is used for content based retrieval has been designed; The first version of a content based search engine has been coded which takes the source of the internet pages, parses the metatags of images and downloads the files in a loop controlled by our texture algorithms. The preprocessing algorithms and the pattern analysis algorithms required for the robustness of the textural feature processing have been implemented. In the last section, the most important textural feature extraction methods have been studied in detail with the performance results of the codes written in Matlab and run on different databases developed