Automatic image annotation and retrieval using subspace clustering algorithm

The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. However, image data usually have a large number of dimensions. Traditional clustering algorithms assign equal weights to these dimensions, and become confounded in the process of dealing with these dimensions. In this paper, we propose weighted feature selection algorithm as a solution to this problem. For a given cluster, we determine relevant features based on histogram analysis and assign greater weight to relevant features as compared to less relevant features. We have implemented various different models to link visual tokens with keywords based on the clustering results of K-means algorithm with weighted feature selection and without feature selection, and evaluated performance using precision, recall and correspondence accuracy using benchmark dataset. The results show that weighted feature selection is better than traditional ones for automatic image annotation and retrieval.

Treatment based on the syndrome differentiation is the key of traditional Chinese medicine (TCM) treating acquired immune deficiency syndrome (AIDS). Syndrome differentiation, where the patients suffering from a western medicine disease are divided into several classes based on their symptoms and signs, is an important diagnostic method and affects the effective use of TCM treatments. Some researches show that the clustering algorithms make it possible to classify the AIDS patients into several syndrome types. These algorithms improve the precision of syndrome differentiation so as to promote the TCM treatment efficacy. However, because of the complexity of AIDS disease, the AIDS clinical data usually have a large number of dimensions. The previous cluster algorithms assign equal weights to these dimensions and become confounded in the process of dealing with these dimensions. In this paper, we use a top-down subspace clustering algorithm as a solution to the syndrome differentiation. For a given cluster, we determine the relevant symptoms based on histogram analysis and assign greater weight to the relevant symptoms as compared to less relevant symptoms. Then, the symptoms with greater weight are used to differentiate the syndrome type of AIDS patients. Finally, the proposed method is compared with the traditional k-means algorithm based on the collected AIDS dataset. We evaluate their performance by the precision and the consistency. The experimental results show that the proposed algorithm is better than the traditional ones for aided TCM syndrome differentiation of AIDS patients.

Advances made to the traditional clustering algorithms solves the various problems such as curse of dimensionality and sparsity of data for multiple attributes. The traditional H-K clustering algorithm can solve the randomness and apriority of the initial centers of K-means clustering algorithm. But when we apply it to high dimensional data it causes the dimensional disaster problem due to high computational complexity. All the advanced clustering algorithms like subspace and ensemble clustering algorithms improve the performance for clustering high dimension dataset from different aspects in different extent. Still these algorithms will improve the performance form a single perspective. The objective of the proposed model is to improve the performance of traditional H-K clustering and overcome the limitations such as high computational complexity and poor accuracy for high dimensional data by combining the three different approaches of clustering algorithm as subspace clustering algorithm and ensemble clustering algorithm with H-K clustering algorithm.

Multimodal image retrieval, which involves retrieving images using various modalities such as text, audio, or other images, has significant research importance due to its wide-ranging applications and potential to enhance user experiences across multiple domains. Traditional image retrieval systems, content-based image retrieval (CBIR) systems rely solely on visual features, which can be limiting. By integrating multiple modalities, multimodal retrieval systems can offer more robust and accurate results. A research problem is considered multimodal when it integrates information from more than one type of data source. In multimodal image retrieval (MMIR) systems, one form of data is used to search for outcomes in the same or different modalities. In contrast, cross-modal systems strictly retrieve information from a different modality. A significant challenge in these systems is the effective comparison of input-output queries from different data types, due to their basic forms and the subjective nature of content similarity. Researchers have proposed various techniques to address this challenge and to bridge the semantic gap in information retrieval across different modalities. This article presents comprehensive analysis of various research works pertained in this multimodal image retrieval. The results and comparative analysis of current research works on benchmark datasets have also been discussed. At the end of the paper, some of open issues are presented for future research directions.

In this paper, we design and construct a multi-label image annotation and retrieval system. Various MPEG-7 low level visual features are employed for representing images. For image annotation, a bi-coded genetic algorithm is employed to select optimal feature subsets and corresponding optimal weights for every one vs. one SVM classifiers. After an unlabelled image is segmented into several regions, pre-trained SVMs are used to annotate each region, final label is obtained by merging all the region labels. Based on multi-label of image, image retrieval system provides keyword-based image retrieval service. Multi-labels can provide abundant descriptions for image content in semantic level, high precision annotation algorithm further improve annotation performance.

Image indexing and retrieval using signature trees

Content-based Image retrieval systems extract and retrieve images using their low-level features, such as color, texture, and shape. Nevertheless, these visual contents do not allow a user to formulate semantically meaningful image query. Image annotation systems are a solution to solve the inadequacy of CBIR systems and allow text based image retrieval. There have been several studies on automatic image annotation utilizing machine learning techniques and images' representation with low level features extracted using either global or local methods. However, typically, these approaches suffer from low correlation between the globally assigned annotations and the visual features used to obtain annotations automatically. In this paper, we present an approach to enhance the effectiveness of CBIR using learning based automatic images annotation based on bag of visual word images representation that is created automatically using a set of manually annotated training images. The experimentation is performed with 4,000 annotated images for training, 1000 images for testing from ImageNet. The result has shown 77.5% of performance accuracy. The result of this work is believed to be one step towards enhancing the performance and effectiveness of existing CBIR and minimizing the semantic gap.

Content-based image retrieval (CBIR) techniques will be part of the next generation of search engines. However, even with a suitable retrieval method, real-time image retrieval from a large image database still requires extremely high computational power. In this paper, a Web-based image database indexing and retrieval system is described. It uses fast CBIR with Beowulf-Class parallel computing engine (Super Abacus) to efficiently and effectively shorten the retrieval time. Content-based image retrieval with several parallel algorithms for images which query different color spaces and the compressed-domain of the images are developed to achieve higher efficiency. Text-based query is also supported for images using a conventional annotation approach.

Problem statement: Recently, there has been a huge progress in collection of varied image databases in the form of digital. Most of the users found it difficult to search and retrieve required images in large collections. In order to provide an effective and efficient search engine tool, the system has been implemented. In image retrieval system, there is no methodologies have been considered directly to retrieve the images from databases. Instead of that, various visual features that have been considered indirect to retrieve the images from databases. In this system, one of the visual features such as texture that has been considered indirectly into images to extract the feature of the image. That featured images only have been considered for the retrieval process in order to retrieve exact desired images from the databases. Approach: The aim of this study is to construct an efficient image retrieval tool namely, “Content Based Medical Image Retrieval with Texture Content using Gray Level Co-occurrence Matrix (GLCM) and k-Means Clustering algorithms”. This image retrieval tool is capable of retrieving images based on the texture feature of the image and it takes into account the Pre-processing, feature extraction, Classification and retrieval steps in order to construct an efficient retrieval tool. The main feature of this tool is used of GLCM of the extracting texture pattern of the image and k-means clustering algorithm for image classification in order to improve retrieval efficiency. The proposed image retrieval system consists of three stages i.e., segmentation, texture feature extraction and clustering process. In the segmentation process, preprocessing step to segment the image into blocks is carried out. A reduction in an image region to be processed is carried out in the texture feature extraction process and finally, the extracted image is clustered using the k-means algorithm. The proposed system is employed for domain specific based search engine for medical Images such as CT-Scan, MRI-Scan and X-Ray. Results: For retrieval efficiency calculation, conventional measures namely precision and recall were calculated using 1000 real time medical images (100 in each category) from the MATLAB Workspace database. For selected query images from the MATLAB-Image Processing tool Box-Workspace Database, the proposed tool was tested and the precision and recall results were presented. The result indicates that the tool gives better performance in terms of percentage for all the 1000 real time medical images from which the scalable performance of the system has been proved. Conclusion: This study proposed a model for the Content Based Medical Image Retrieval System by using texture feature in calculating the Gray Level Co Occurrence matrix (GLCM) from which various statistical measures were computed in order to increasing similarities between query image and database images for improving the retrieval performance along with the large scalability of the databases.

Modified FDP cluster algorithm and its application in protein conformation clustering analysis

Many different approaches have been taken, some focusing on highly specific topics such as improved pattern-matching algorithms, others taking a much broader view, and attempting to study the issues involved by developing and evaluating prototype systems. Work on image retrieval at Northumbria University falls mainly into this second category. Our long-term aim is to investigate the principles on which workable shape retrieval systems can be constructed. Our approach has been essentially pragmatic to develop a series of steadily more sophisticated prototypes capable of handling an increasingly broad range of image types and retrieval paradigms. Our first project, the development and evaluation of SAFARI (Shape Analysis For Automatic Retrieval of Images), a prototype shape retrieval system for two dimensional engineering drawings of simple machined parts, demonstrated that we could achieve excellent retrieval performance within a restricted class of images. The aim of our current ARTISAN (Automatic Retrieval of Trademark Images by Shape ANalysis) project is twofold: to develop and evaluate a prototype shape retrieval system for trade mark images consisting of abstract geometric designs, and to assess the feasibility of generalizing the image retrieval approach developed in our earlier SAFARI system to a wider range of image types. It is hoped that the prototype system developed will provide a basis from which the Patent Office Trade Marks Registry can develop an improved image retrieval system for their own use.

Background: Retrieval of facial images based on its contents is one of the main areas of research. However, images contain high dimensional feature vectors and it is a challenging task to select the relevant features due to the variations available in the images of similar objects. Therefore, the selection of relevant features is an important step to make the facial retrieval system computationally efficient and more accurate. Objective: The main aim of this paper is to design and develop an efficient feature selection method for obtaining relevant and non-redundant features from the face images so that the accuracy and computational cost of a face retrieval system can be improved. Methods: The proposed feature selection method uses a new enhanced grasshopper optimization algorithm to obtain the significant features from the high dimensional features vector of face images. The proposed algorithm modifies the target vector by considering more than one best solution which maintain the elitism property and save the search from local optimum. Furthermore, it has been utilized to select the prominent features from the high dimensional facial features vector. Results: The performance of the proposed feature selection method has been tested on Oracle Research Laboratory face database. The proposed method eliminates 89% features which are minimum among the other methods and increases the accuracy of face retrieval system to 96.5%. Conclusion: The enhanced grasshopper optimization algorithm-based feature selection method for face retrieval system outperforms the existing methods in terms of accuracy and computational cost.

. We introduce a very fast and reliable method for shape similarity retrieval in large image databases which is robust with respect to noise, scale and orientation changes of the objects. The maxima of curvature zero crossing contours of Curvature Scale Space (CSS) image are used to represent the shapes of object boundary contours. While a complex boundary is represented by about five pairs of integer values, an effective indexing method based on the aspect ratio of the CSS image, eccentricity and circularity is used to narrow down the range of searching. Since the matching algorithm has been designed to use global information, it is sensitive to major occlusion, but some minor occlusion will not cause any problems. We have tested and evaluated our method on a prototype database of 450 images of marine animals with a vast variety of shapes with very good results. The method can either be used in real applications or produce a reliable shape description for more complicated images when ...

Retrieving hidden information in image databases is a difficult task because of their complex structure and the subjectivity related to their interpretation. In this situation the use of an index is primordial. We propose an effective method for locally updating neighborhood graphs which constitute our index. This method is based on an intelligent way for locating points in a multidimensional space. Promising results are obtained after experimentations on various databases.

It has been heavy work that to find the related pictures form Internet without annotation. Therefore, the automatic image annotation was extremely important in image retrieval. The traditional method were translated image visual feature into keywords simply, but it ignored the image similarity problem between the low-level visual features and high-level semantic. That is image "gap" problem, so image annotation was very lower. This paper puts forward a classification of web based image content automatic tagging mixing technology, the first it will map visual feature of image to one or more rough images, then we will preprocess the web page text information, finally we select some keywords similarity as image annotation by using similar semantic processing module. So it realizes the image and text combining the automatic annotation and it achieve high precision of image annotation.