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

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

Plant image retrieval using color, shape and texture features

We present a content-based image retrieval system for plant image retrieval, intended especially for the house plant identification problem. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging.We studied the suitability of various well-known color, shape and texture features for this problem, as well as introducing some new texture matching techniques and shape features. Feature extraction is applied after segmenting the plant region from the background using the max-flow min-cut technique. Results on a database of 380 plant images belonging to 78 different types of plants show promise of the proposed new techniques and the overall system: in 55% of the queries, the correct plant image is retrieved among the top-15 results. Furthermore, the accuracy goes up to 73% when a 132-image subset of well-segmented plant images are considered

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

A Sub-block Based Image Retrieval Using Modified Integrated Region Matching

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding followed by morphological dilation. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. The colour and texture feature vectors is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). A modified Integrated Region Matching (IRM) algorithm is used for finding the minimum distance between the sub-blocks of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods.Comment: 7 page