Structured Knowledge Representation for Image Retrieval

We propose a structured approach to the problem of retrieval of images by content and present a description logic that has been devised for the semantic indexing and retrieval of images containing complex objects. As other approaches do, we start from low-level features extracted with image analysis to detect and characterize regions in an image. However, in contrast with feature-based approaches, we provide a syntax to describe segmented regions as basic objects and complex objects as compositions of basic ones. Then we introduce a companion extensional semantics for defining reasoning services, such as retrieval, classification, and subsumption. These services can be used for both exact and approximate matching, using similarity measures. Using our logical approach as a formal specification, we implemented a complete client-server image retrieval system, which allows a user to pose both queries by sketch and queries by example. A set of experiments has been carried out on a testbed of images to assess the retrieval capabilities of the system in comparison with expert users ranking. Results are presented adopting a well-established measure of quality borrowed from textual information retrieval

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

Color image retrieval using taken images

Now-a-days in many applications content based image retrieval from large resources has become an area of wide interest. In this paper we present a color-based image retrieval system that uses color and texture as visual features to describe the content of an image region. To speed up retrieval and similarity computation, the database images are segmented and the extracted regions are clustered according to their feature vectors. This process is performed offline before query processing, therefore to answer a query our system need not search the entire database images; instead just a number of candidate images are required to be searched for image similarity. Our proposed system has the advantage of increasing the retrieval accuracy and decreasing the retrieval time. The experimental evaluation of the system is based on a 1,000 real taken color image database. From the experimental results, it is evident that our system performs significantly better and faster compared with other existing systems. In our analysis, we provide a comparison between retrieval results based on relevancy for the given ten classes. The results demonstrate that each type of feature is effective for a particular type of images according to its semantic contents, and using a combination of them gives better retrieval results for almost all semantic classes

Multi-resolution shape-based image retrieval using Ridgelet transform

Complicated shapes can be effectively characterized using multi-resolution descriptors. One popular method is the Ridgelet transform which has enjoyed very little exposure in describing shapes for Content-based Image Retrieval (CBIR). Many of the existing Ridgelet transforms are only applied on images of size M×M. For M×N sized images, they need to be segmented into M×M sub-images prior to processing. A different number of orientations and cut-off points for the Radon transform parameters also need to be utilized according to the image size. This paper presents a new shape descriptor for CBIR based on Ridgelet transform which is able to handle images of various sizes. The utilization of the ellipse template for better image coverage and the normalization of the Ridgelet transform are introduced. For better retrieval, a template-option scheme is also introduced. Retrieval effectiveness obtained by the proposed method has shown to be higher compared to several previous descriptors

Generalized Ridgelet-Fourier for M×N images: determining the normalization criteria

Ridgelet transform (RT) has gained its popularity due to its capability in dealing with line singularities effectively. Many of the existing RT however is only applied to images of size M×M or the M×N images will need to be pre-segmented into M×M sub-images prior to processing. The research presented in this article is aimed at the development of a generalized RT for content-based image retrieval so that it can be applied easily to any images of various sizes. This article focuses on comparing and determining the normalization criteria for Radon transform, which will aid in achieving the aim. The Radon transform normalization criteria sets are compared and evaluated on an image database consisting of 216 images, where the precision and recall and Averaged Normalized Modified Retrieval Rank (ANMRR) are measured

Towards Content-based Pixel Retrieval in Revisited Oxford and Paris

This paper introduces the first two pixel retrieval benchmarks. Pixel retrieval is segmented instance retrieval. Like semantic segmentation extends classification to the pixel level, pixel retrieval is an extension of image retrieval and offers information about which pixels are related to the query object. In addition to retrieving images for the given query, it helps users quickly identify the query object in true positive images and exclude false positive images by denoting the correlated pixels. Our user study results show pixel-level annotation can significantly improve the user experience. Compared with semantic and instance segmentation, pixel retrieval requires a fine-grained recognition capability for variable-granularity targets. To this end, we propose pixel retrieval benchmarks named PROxford and PRParis, which are based on the widely used image retrieval datasets, ROxford and RParis. Three professional annotators label 5,942 images with two rounds of double-checking and refinement. Furthermore, we conduct extensive experiments and analysis on the SOTA methods in image search, image matching, detection, segmentation, and dense matching using our pixel retrieval benchmarks. Results show that the pixel retrieval task is challenging to these approaches and distinctive from existing problems, suggesting that further research can advance the content-based pixel-retrieval and thus user search experience. The datasets can be downloaded from \href{https://github.com/anguoyuan/Pixel_retrieval-Segmented_instance_retrieval}{this link}

Structured Knowledge Representation for Image Retrieval

We propose a structured approach to the problem of retrieval of images by content and present a description logic that has been devised for the semantic indexing and retrieval of images containing complex objects. As other approaches do, we start from low-level features extracted with image analysis to detect and characterize regions in an image. However, in contrast with feature-based approaches, we provide a syntax to describe segmented regions as basic objects and complex objects as compositions of basic ones. Then we introduce a companion extensional semantics for defining reasoning services, such as retrieval, classification, and subsumption. These services can be used for both exact and approximate matching, using similarity measures. Using our logical approach as a formal specification, we implemented a complete clientserver image retrieval system, which allows a user to pose both queries by sketch and queries by example. A set of experiments has been carried out on a testbed of images to assess the retrieval capabilities of the system in comparison with expert users ranking. Results are presented adopting a well-established measure of quality borrowed from textual information retrieval

PENGGUNAAN FITUR WARNA DAN TEKSTUR UNTUK CONTENT BASED IMAGE RETRIEVAL CITRA BUNGA

Pencarian gambar berdasarkan gambar pada database, seringkali dilakukan untuk mengatasi duplikasi pada suatu karya. Content Based Image Retrieval (CBIR) Citra Bunga adalah engine pada komputer untuk melakukan pencarian gambar berdasarkan gambar pada database. Penelitian pada Content Based Image Retrieval (CBIR) Citra Bunga telah dilakukan oleh banyak peneliti. Permasalahan terjadi ketika memilih metode pendekatan seperti preprocessing, ekstraksi fitur dan similarity measure pada CBIR Citra Bunga. Pendekatan yang tidak sesuai dengan data yang diuji, tidak akan memberikan hasil yang optimal. Untuk mengetahui tingkat keberhasilan pendekatan yang digunakan pada CBIR Citra Bunga, digunakan perhitungan nilai precision. Pada penelitian ini, dataset yang akan digunakan adalah dataset Oxford Flower 17. Berdasarkan penelitian sebelumnya, untuk mendapatkan nilai precision yang lebih baik, penelitian ini akan menggunakan ekstraksi fitur warna Hue Saturation Value (HSV), ekstraksi fitur tekstur Gray Level Co-occurrence Matrix (GLCM), dan gabungan kedua fitur dengan pendekatan histogram. Pada penelitian CBIR Citra Bunga ini, terdapat tiga proses yaitu segmentasi menggunakan thresholding, proses ekstraksi fitur, dan pengukuran tingkat kemiripan citra dengan Euclidean Distance. Pengujian pada sistem dilakukan berdasarkan citra yang tersegmentasi dan tidak tersegmentasi. Pengujian sistem dengan hasil Mean Average Precision (MAP) terbesar dihasilkan oleh proses ekstraksi fitur GLCM tidak tersegmentasi sebesar 87,32%, dan untuk nilai MAP terbesar pada citra tersegmentasi dihasilkan pada proses ekstraksi fitur HSV sebesar 83,35%. Kata kunci: Content Based Image Retrieval, ekstraksi fitur HSV, ekstraksi fitur GLCM, thresholding, Euclidean Distance, Mean Average Precision (MAP);--- Searching images based on images in the database, often done to overcome duplication of a work. Content Based Image Retrieval (CBIR) Flower Image is the engine on the computer To perform image-based image search on the database. Research on Content Based Image Retrieval (CBIR) Flower Image has been done by many researchers. Problems occur when choosing approaches such as preprocessing, feature extraction and similarity measure in CBIR Flower Image. Approaches which don't correspond with the data image test, would not provide optimal results. To know the success rate of approach used in CBIR Flower Image, the calculation of precision value is used. In this study, the dataset that will be used is dataset Oxford Flower 17. Based on previous research, to get better precision value, this research will use Hue Saturation Value (HSV) feature extraction, feature extraction of Gray Level Co-occurrence Matrix (GLCM) texture, and combination of both features with histogram approach. In this research, there are three processes: segmentation using thresholding, feature extraction process, and measurement of image similarity level with Euclidean Distance. For testing the system, is based on segmented image and non-segmented image. The result of the largest Mean Average Precision (MAP) produced in this study, resulted from the process of unsegmented image by the GLCM feature extraction of 87.32%, and for the largest MAP value in the segmented image produced by the HSV feature extraction process of 83.35%. Keywords: Content Based Image Retrieval, feature extraction HSV, feature extraction GLCM, thresholding, Euclidean Distance, Mean Average Precision (MAP

Segmentation for Image Indexing and Retrieval on Discrete Cosines Domain

This paper used region growing segmentation technique to segment the Discrete Cosines (DC) image. The classic problem of content Based image retrieval (CBIR) is the lack of accuracy in matching between image query and image in the database. By using region growing technique on DC image,it reduced the number of image regions indexed. The proposed of recursive region growing is not new technique but its application on DC images to build  indexing keys is quite new and not yet presented by many  authors. The experimental results show that the proposed methods on segmented images present good precision which are higher than 0.60 on all classes. So, it could be concluded that region growing segmented based CBIR more efficient   compared to DC images in term of their precision 0.59 and 0.75, respectively. Moreover, DC based CBIR can save time and simplify algorithm compared to DCT images. The most significant finding from this work is instead of using 64 DCT coefficients this research only used 1/64 coefficients which is DC coefficient.