Color image quality measures and retrieval

The focus of this dissertation is mainly on color image, especially on the images with lossy compression. Issues related to color quantization, color correction, color image retrieval and color image quality evaluation are addressed. A no-reference color image quality index is proposed. A novel color correction method applied to low bit-rate JPEG image is developed. A novel method for content-based image retrieval based upon combined feature vectors of shape, texture, and color similarities has been suggested. In addition, an image specific color reduction method has been introduced, which allows a 24-bit JPEG image to be shown in the 8-bit color monitor with 256-color display. The reduction in download and decode time mainly comes from the smart encoder incorporating with the proposed color reduction method after color space conversion stage. To summarize, the methods that have been developed can be divided into two categories: one is visual representation, and the other is image quality measure. Three algorithms are designed for visual representation: (1) An image-based visual representation for color correction on low bit-rate JPEG images. Previous studies on color correction are mainly on color image calibration among devices. Little attention was paid to the compressed image whose color distortion is evident in low bit-rate JPEG images. In this dissertation, a lookup table algorithm is designed based on the loss of PSNR in different compression ratio. (2) A feature-based representation for content-based image retrieval. It is a concatenated vector of color, shape, and texture features from region of interest (ROI). (3) An image-specific 256 colors (8 bits) reproduction for color reduction from 16 millions colors (24 bits). By inserting the proposed color reduction method into a JPEG encoder, the image size could be further reduced and the transmission time is also reduced. This smart encoder enables its decoder using less time in decoding. Three algorithms are designed for image quality measure (IQM): (1) A referenced IQM based upon image representation in very low-dimension. Previous studies on IQMs are based on high-dimensional domain including spatial and frequency domains. In this dissertation, a low-dimensional domain IQM based on random projection is designed, with preservation of the IQM accuracy in high-dimensional domain. (2) A no-reference image blurring metric. Based on the edge gradient, the degree of image blur can be measured. (3) A no-reference color IQM based upon colorfulness, contrast and sharpness

Geographical information retrieval with ontologies of place

Geographical context is required of many information retrieval tasks in which the target of the search may be documents, images or records which are referenced to geographical space only by means of place names. Often there may be an imprecise match between the query name and the names associated with candidate sources of information. There is a need therefore for geographical information retrieval facilities that can rank the relevance of candidate information with respect to geographical closeness of place as well as semantic closeness with respect to the information of interest. Here we present an ontology of place that combines limited coordinate data with semantic and qualitative spatial relationships between places. This parsimonious model of geographical place supports maintenance of knowledge of place names that relate to extensive regions of the Earth at multiple levels of granularity. The ontology has been implemented with a semantic modelling system linking non-spatial conceptual hierarchies with the place ontology. An hierarchical spatial distance measure is combined with Euclidean distance between place centroids to create a hybrid spatial distance measure. This is integrated with thematic distance, based on classification semantics, to create an integrated semantic closeness measure that can be used for a relevance ranking of retrieved objects

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

Organising and structuring a visual diary using visual interest point detectors

As wearable cameras become more popular, researchers are increasingly focusing on novel applications to manage the large volume of data these devices produce. One such application is the construction of a Visual Diary from an individual’s photographs. Microsoft’s SenseCam, a device designed to passively record a Visual Diary and cover a typical day of the user wearing the camera, is an example of one such device. The vast quantity of images generated by these devices means that the management and organisation of these collections is not a trivial matter. We believe wearable cameras, such as SenseCam, will become more popular in the future and the management of the volume of data generated by these devices is a key issue. Although there is a significant volume of work in the literature in the object detection and recognition and scene classification fields, there is little work in the area of setting detection. Furthermore, few authors have examined the issues involved in analysing extremely large image collections (like a Visual Diary) gathered over a long period of time. An algorithm developed for setting detection should be capable of clustering images captured at the same real world locations (e.g. in the dining room at home, in front of the computer in the office, in the park, etc.). This requires the selection and implementation of suitable methods to identify visually similar backgrounds in images using their visual features. We present a number of approaches to setting detection based on the extraction of visual interest point detectors from the images. We also analyse the performance of two of the most popular descriptors - Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF).We present an implementation of a Visual Diary application and evaluate its performance via a series of user experiments. Finally, we also outline some techniques to allow the Visual Diary to automatically detect new settings, to scale as the image collection continues to grow substantially over time, and to allow the user to generate a personalised summary of their data

Enhancing Automatic Annotation for Optimal Image Retrieval

Image search and retrieval based on content is very cumbersome task particularly when the image database is large. The accuracy of the retrieval as well as the processing speed are two important measures used for assessing and comparing the effectiveness of various systems. Text retrieval is more mature and advanced than image content retrieval. In this dissertation, the focus is on converting image content into text tags that can be easily searched using standard search engines where the size and speed issues of the database have been already dealt with. Therefore, image tagging becomes an essential tool for image retrieval from large image databases. Automation of image tagging has received considerable attention by many researchers in recent years. The optimal goal of image description is to automatically annotate images with tags that semantically represent the image content. The speed and accuracy of Image retrieval from large databases are few of the important domains that can benefit from automatic tagging. In this work, several state of the art image classification and image tagging techniques are reviewed. We propose a new self-learning multilayered tagging framework that can address the limitations of current approaches and provide mutual accuracy improvement between the recognition layer and the annotation layer. Our results indicate that the proposed framework can improve the overall accuracy of information retrieval in a variety of image databases

Deep Descriptor Learning with Auxiliary Classification Loss for Retrieving Images of Silk Fabrics in the Context of Preserving European Silk Heritage

With the growing number of digitally available collections consisting of images depicting relevant objects from the past in relation with descriptive annotations, the need for suitable information retrieval techniques is becoming increasingly important to support historians in their work. In this context, we address the problem of image retrieval for searching records in a database of silk fabrics. The descriptors, used as an index to the database, are learned by a convolutional neural network, exploiting the available annotations to automatically generate training data. Descriptor learning is combined with auxiliary classification loss with the aim of supporting the clustering in the descriptor space with respect to the properties of the depicted silk objects, such as the place or time of origin. We evaluate our approach on a dataset of fabric images in a kNN-classification, showing promising results with respect to the ability of the descriptors to represent semantic properties of silk fabrics; integrating the auxiliary loss improves the overall accuracy by 2.7% and the average F1 score by 5.6%. It can be observed that the largest improvements can be obtained for variables with imbalanced class distributions. An evaluation on the WikiArt dataset demonstrates the transferability of our approach to other digital collection

A Method for Determining the Shape Similarity of Complex Three-Dimensional Structures to Aid Decay Restoration and Digitization Error Correction

none5noThis paper introduces a new method for determining the shape similarity of complex three-dimensional (3D) mesh structures based on extracting a vector of important vertices, ordered according to a matrix of their most important geometrical and topological features. The correlation of ordered matrix vectors is combined with perceptual definition of salient regions in order to aid detection, distinguishing, measurement and restoration of real degradation and digitization errors. The case study is the digital 3D structure of the Camino Degli Angeli, in the Urbino’s Ducal Palace, acquired by the structure from motion (SfM) technique. In order to obtain an accurate, featured representation of the matching shape, the strong mesh processing computations are performed over the mesh surface while preserving real shape and geometric structure. In addition to perceptually based feature ranking, the new theoretical approach for ranking the evaluation criteria by employing neural networks (NNs) has been proposed to reduce the probability of deleting shape points, subject to optimization. Numerical analysis and simulations in combination with the developed virtual reality (VR) application serve as an assurance to restoration specialists providing visual and feature-based comparison of damaged parts with correct similar examples. The procedure also distinguishes mesh irregularities resulting from the photogrammetry process.openVasic I.; Quattrini R.; Pierdicca R.; Frontoni E.; Vasic B.Vasic, I.; Quattrini, R.; Pierdicca, R.; Frontoni, E.; Vasic, B