Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval

The aim of a Content-Based Image Retrieval (CBIR) system, also known as Query by Image Content (QBIC), is to help users to retrieve relevant images based on their contents. CBIR technologies provide a method to find images in large databases by using unique descriptors from a trained image. The image descriptors include texture, color, intensity and shape of the object inside an image. Several feature-extraction techniques viz., Average RGB, Color Moments, Co-occurrence, Local Color Histogram, Global Color Histogram and Geometric Moment have been critically compared in this paper. However, individually these techniques result in poor performance. So, combinations of these techniques have also been evaluated and results for the most efficient combination of techniques have been presented and optimized for each class of image query. We also propose an improvement in image retrieval performance by introducing the idea of Query modification through image cropping. It enables the user to identify a region of interest and modify the initial query to refine and personalize the image retrieval results.Comment: 8 pages, 16 figures, 11 table

IMAGE SEARCH ENGINE BASED ON COMBINED FEATURES OF IMAGE SUB-BLOCKS

In this paper we propose a new and efficient technique to retrieve images based on sum of the values of Local Histogram and GLCM (Gray Level Co-occurrence Matrix) texture of image sub-blocks to enhance the retrieval performance. The image is divided into sub blocks of equal size. Then the color and texture features of each sub-block are computed. Most of the image retrieval techniques used Histograms for indexing. Histograms describe global intensity distribution. They are very easy to compute and are insensitive to small changes in object translations and rotations. Our main focus is on separation of the image bins (histogram value divisions by frequency) followed by calculating the sum of values, and using them as image local features. At first, the histogram is calculated for an image sub-block. After that, it is subdivided into 16 equal bins and the sum of local values is calculated and stored. Similarly the texture features are extracted based on GLCM. The four statistic features of GLCM i.e. entropy, energy, inverse difference and contrast are used as texture features. These four features are computed in four directions (00, 450, 900, and 1350). A total of 16 texture values are computed per an image sub-block. An integrated matching scheme based on Most Similar Highest Priority (MSHP) principle is used to compare the query and target image. The adjacency matrix of a bipartite graph is formed using the sub-blocks of query and target image. This matrix is used for matching the images. Sum of the differences between each bin of the query and target image histogram is used as a distance measure for Local Histogram and Euclidean distance is adopted for texture features. Weighted combined distance is used in retrieving the images. The experimental results show that the proposed method has achieved highest retrieval performance

Implementasi Temu Kembali Citra Menggunakan Fitur Warna Berbasis Histogram dan Fitur Tekstur Berbasis Blok

Citra digital biasa digunakan masyarakat dalam berbagai bidang seperti kesehatan, perdagangan, dan hiburan. Hal ini menyebabkan meningkatnya citra digital yang dihasilkan setiap harinya. Citra digital yang dihasilkan kemudian disimpan dalam suatu tempat penyimpanan seperti database. Banyaknya citra digital yang disimpan dalam database menyebabkan sulitnya pengelolaan file-file citra terutama dalam menemukan konten citra yang diinginkan. Content based image retrieval (CBIR) merupakan sebuah metode pencarian citra dengan melakukan perbandingan antara citra query dengan citra yang ada di database berdasarkan informasi yang ada pada citra tersebut. Pada tugas akhir ini, dibangun suatu sistem temu kembali citra menggunakan fitur warna berbasis histogram dan fitur tekstur berbasis blok. Metode histogram warna digunakan untuk ekstraksi fitur warna dan ekstraksi Block Difference of Inverse Probabilities dan Block Variation of Local Correlation Coefficients digunakan untuk mengekstraksi fitur tekstur. Metode Square Chord Distance digunakan untuk menghitung jarak citra. Hasil pencarian citra mirip dengan rata-rata precision terbaik didapatkan dari perpaduan ekstraksi fitur warna dan tekstur warna dengan rata-rata precision 93.71% dan rata-rata waktu komputasi 0.2281 detik. Sedangkan untuk perpaduan ekstraksi dengan hasil rata-rata waktu komputasi terbaik adalah menggunakan perpaduan ekstraksi fitur warna dan tekstur brightness dengan rata-rata precision 92.22% dan rata-rata waktu komputasi 0.1468 detik. ================================================================================================= Digital imagery is commonly used by people in the fields of health, commerce, and entertainment. Digital images are usually stored in a storage place such as a database. The large number of digital images stored in the database causes the difficulty of managing image files, especially in finding the desired image content. Content based image retrieval (CBIR) is an image search method by performing a comparison between the image of the query and the image in the database based on the information contained in the image. In this final project, an image retrieval system were built using histogram-based color features and block-based texture features. Color histogram method is used for color feature extraction. Block Difference of Inverse Probabilities and Block Variations of Local Correlation Coefficients are used to extract texture features. Square Chord Distance method is used to calculate the distance of the image. The best precision of image retrieval were obtained from the combination of color extraction and color texture extraction with average precision 93.71% and the average computation time 0.2281 seconds. As for the best average computation time of image retrieval were obtained from combination of color feature and brightness texture with average precision 93.71% and the average computation time 0.1468 seconds

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

Comparative analysis of image search algorithm using average RGB, local color histogram, global color histogram and color moment HSV

Image retrieval forms a major problem when a large database is considered. Content Base Image Retrieval (CBIR) makes use of the available visual features of the image and helps in retrieving similar image as that of the query image. In the CBIR method, each image stored in the database has its features extracted and compared to the features of the query image. Thus, it involves two processes, feature extraction and feature matching. In this thesis, four techniques have been used, which are the Average of Red, Green and Blue Color Channels (Average RGB), Local Color Histogram (LCH), Global Color Histogram (GCH) and Color Moment of Hue, Saturation and Brightness Value (HSV) to retrieve relevant images based on colour. These techniques are applied on the collection of three images chosen randomly from each class of Wang images database. The performance of each technique has been individually evaluated, in terms of Execution Time, Precision, Recall, Accuracy, Redundancy Factor and Fall Rate. The results were then analysed and compared. The comparison was shown in bar graphs that the Average RGB technique has the best performance, where it obtained high accuracy. As a conclusion to the report, this comparative study contributes to the image searching field, by measuring the performance for several CBIR techniques using more commonly used parameters

Region Based Image Retrieval Using Ratio of Proportional Overlapping Object

In Region Based Image Retrieval (RBIR), determination of the relevant block in query region is based on the percentage of image objects that overlap with each sub-blocks. But in some images, the size of relevant objects are small. It may cause the object to be ignored in determining the relevant sub-blocks. Therefore, in this study we proposed a system of RBIR based on the percentage of proportional objects that overlap with sub-blocks. Each sub-blocks is selected as a query region. The color and texture features of the query region will be extracted by using HSV histogram and Local Binary Pattern (LBP), respectively. We also used shape as global feature by applying invariant moment as descriptor. Experimental results show that the proposed method has average precision with 74%

Autoencoding the Retrieval Relevance of Medical Images

Content-based image retrieval (CBIR) of medical images is a crucial task that can contribute to a more reliable diagnosis if applied to big data. Recent advances in feature extraction and classification have enormously improved CBIR results for digital images. However, considering the increasing accessibility of big data in medical imaging, we are still in need of reducing both memory requirements and computational expenses of image retrieval systems. This work proposes to exclude the features of image blocks that exhibit a low encoding error when learned by a $n/p/n$ autoencoder ($p\!<\!n$). We examine the histogram of autoendcoding errors of image blocks for each image class to facilitate the decision which image regions, or roughly what percentage of an image perhaps, shall be declared relevant for the retrieval task. This leads to reduction of feature dimensionality and speeds up the retrieval process. To validate the proposed scheme, we employ local binary patterns (LBP) and support vector machines (SVM) which are both well-established approaches in CBIR research community. As well, we use IRMA dataset with 14,410 x-ray images as test data. The results show that the dimensionality of annotated feature vectors can be reduced by up to 50% resulting in speedups greater than 27% at expense of less than 1% decrease in the accuracy of retrieval when validating the precision and recall of the top 20 hits.Comment: To appear in proceedings of The 5th International Conference on Image Processing Theory, Tools and Applications (IPTA'15), Nov 10-13, 2015, Orleans, Franc