Visual intelligence for online communities : commonsense image retrieval by query expansion

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2004.Includes bibliographical references (leaves 65-67).This thesis explores three weaknesses of keyword-based image retrieval through the design and implementation of an actual image retrieval system. The first weakness is the requirement of heavy manual annotation of keywords for images. We investigate this weakness by aggregating the annotations of an entire community of users to alleviate the annotation requirements on the individual user. The second weakness is the hit-or-miss nature of exact keyword matching used in many existing image retrieval systems. We explore this weakness by using linguistics tools (WordNet and the OpenMind Commonsense database) to locate image keywords in a semantic network of interrelated concepts so that retrieval by keywords is automatically expanded semantically to avoid the hit-or-miss problem. Such semantic query expansion further alleviates the requirement for exhaustive manual annotation. The third weakness of keyword-based image retrieval systems is the lack of support for retrieval by subjective content. We investigate this weakness by creating a mechanism to allow users to annotate images by their subjective emotional content and subsequently to retrieve images by these emotions. This thesis is primarily an exploration of different keyword-based image retrieval techniques in a real image retrieval system. The design of the system is grounded in past research that sheds light onto how people actually encounter the task of describing images with words for future retrieval. The image retrieval system's front-end and back- end are fully integrated with the Treehouse Global Studio online community - an online environment with a suite of media design tools and database storage of media files and metadata.(cont.) The focus of the thesis is on exploring new user scenarios for keyword-based image retrieval rather than quantitative assessment of retrieval effectiveness. Traditional information retrieval evaluation metrics are discussed but not pursued. The user scenarios for our image retrieval system are analyzed qualitatively in terms of system design and how they facilitate the overall retrieval experience.James Jian Dai.S.M

Perceptually relevant browsing environments for large texture databases

This thesis describes the development of a large database of texture stimuli, the production of a similarity matrix re ecting human judgements of similarity about the database, and the development of three browsing models that exploit structure in the perceptual information for navigation. Rigorous psychophysical comparison experiments are carried out and the SOM (Self Organising Map) found to be the fastest of the three browsing models under examination. We investigate scalable methods of augmenting a similarity matrix using the SOM browsing environment to introduce previously unknown textures. Further psychophysical experiments reveal our method produces a data organisation that is as fast to navigate as that derived from the perceptual grouping experiments.Engineering and Physical Sciences Research Council (EPSRC

A Method Of Content-based Image Retrieval For The Generation Of Image Mosaics

An image mosaic is an artistic work that uses a number of smaller images creatively combined together to form another larger image. Each building block image, or tessera, has its own distinctive and meaningful content, but when viewed from a distance the tesserae come together to form an aesthetically pleasing montage. This work presents the design and implementation of MosaiX, a computer software system that generates these image mosaics automatically. To control the image mosaic creation process, several parameters are used within the system. Each parameter affects the overall mosaic quality, as well as required processing time, in its own unique way. A detailed analysis is performed to evaluate each parameter individually. Additionally, this work proposes two novel ways by which to evaluate the quality of an image mosaic in a quantitative way. One method focuses on the perceptual color accuracy of the mosaic reproduction, while the other concentrates on edge replication. Both measures include preprocessing to take into account the unique visual features present in an image mosaic. Doing so minimizes quality penalization due the inherent properties of an image mosaic that make them visually appealing

EBCOT ALGORITHM BASED INVERSE HALFTONNING ON CBIR

A procedure for Content Based Image Retrieval (CBIR) for the formation of picture content descriptor which abusing the upside of low multifaceted nature Order Dither Block Truncation Coding (ODBTC). The quantizer and bitmap picture are the packed type of picture got from the ODBTC method in encoding step. Translating isn't acted in this strategy. It has two picture highlight, for example, Color Co-event Feature (CCF) and Bit Pattern Feature (BPF) for ordering the picture. These highlights are straightforwardly gotten from ODBTC encoded information stream. By contrasting and the BTC picture recovery framework and other prior technique the test result show the proposed strategy is predominant. ODBTC is appropriate for picture pressure and it is a simple and viable descriptor to file the picture in CBIR. Content-based picture recovery is utilized to separate the pictures based on their substance, for example, surface, shading, shape and spatial format. To limit this hole numerous ideas was presented. Also, Images can be put away and extricated dependent on different highlights and one of the unmistakable component is Texture

Colour-based image retrieval algorithms based on compact colour descriptors and dominant colour-based indexing methods

Content based image retrieval (CBIR) is reported as one of the most active research areas in the last two decades, but it is still young. Three CBIR’s performance problem in this study is inaccuracy of image retrieval, high complexity of feature extraction, and degradation of image retrieval after database indexing. This situation led to discrepancies to be applied on limited-resources devices (such as mobile devices). Therefore, the main objective of this thesis is to improve performance of CBIR. Images’ Dominant Colours (DCs) is selected as the key contributor for this purpose due to its compact property and its compatibility with the human visual system. Semantic image retrieval is proposed to solve retrieval inaccuracy problem by concentrating on the images’ objects. The effect of image background is reduced to provide more focus on the object by setting weights to the object and the background DCs. The accuracy improvement ratio is raised up to 50% over the compared methods. Weighting DCs framework is proposed to generalize this technique where it is demonstrated by applying it on many colour descriptors. For reducing high complexity of colour Correlogram in terms of computations and memory space, compact representation of Correlogram is proposed. Additionally, similarity measure of an existing DC-based Correlogram is adapted to improve its accuracy. Both methods are incorporated to produce promising colour descriptor in terms of time and memory space complexity. As a result, the accuracy is increased up to 30% over the existing methods and the memory space is decreased to less than 10% of its original space. Converting the abundance of colours into a few DCs framework is proposed to generalize DCs concept. In addition, two DC-based indexing techniques are proposed to overcome time problem, by using RGB and perceptual LUV colour spaces. Both methods reduce the search space to less than 25% of the database size with preserving the same accuracy

Scene Segmentation and Object Classification for Place Recognition

This dissertation tries to solve the place recognition and loop closing problem in a way similar to human visual system. First, a novel image segmentation algorithm is developed. The image segmentation algorithm is based on a Perceptual Organization model, which allows the image segmentation algorithm to ‘perceive’ the special structural relations among the constituent parts of an unknown object and hence to group them together without object-specific knowledge. Then a new object recognition method is developed. Based on the fairly accurate segmentations generated by the image segmentation algorithm, an informative object description that includes not only the appearance (colors and textures), but also the parts layout and shape information is built. Then a novel feature selection algorithm is developed. The feature selection method can select a subset of features that best describes the characteristics of an object class. Classifiers trained with the selected features can classify objects with high accuracy. In next step, a subset of the salient objects in a scene is selected as landmark objects to label the place. The landmark objects are highly distinctive and widely visible. Each landmark object is represented by a list of SIFT descriptors extracted from the object surface. This object representation allows us to reliably recognize an object under certain viewpoint changes. To achieve efficient scene-matching, an indexing structure is developed. Both texture feature and color feature of objects are used as indexing features. The texture feature and the color feature are viewpoint-invariant and hence can be used to effectively find the candidate objects with similar surface characteristics to a query object. Experimental results show that the object-based place recognition and loop detection method can efficiently recognize a place in a large complex outdoor environment

Using contour information and segmentation for object registration, modeling and retrieval

This thesis considers different aspects of the utilization of contour information and syntactic and semantic image segmentation for object registration, modeling and retrieval in the context of content-based indexing and retrieval in large collections of images. Target applications include retrieval in collections of closed silhouettes, holistic w ord recognition in handwritten historical manuscripts and shape registration. Also, the thesis explores the feasibility of contour-based syntactic features for improving the correspondence of the output of bottom-up segmentation to semantic objects present in the scene and discusses the feasibility of different strategies for image analysis utilizing contour information, e.g. segmentation driven by visual features versus segmentation driven by shape models or semi-automatic in selected application scenarios. There are three contributions in this thesis. The first contribution considers structure analysis based on the shape and spatial configuration of image regions (socalled syntactic visual features) and their utilization for automatic image segmentation. The second contribution is the study of novel shape features, matching algorithms and similarity measures. Various applications of the proposed solutions are presented throughout the thesis providing the basis for the third contribution which is a discussion of the feasibility of different recognition strategies utilizing contour information. In each case, the performance and generality of the proposed approach has been analyzed based on extensive rigorous experimentation using as large as possible test collections

Trademark image retrieval by local features

The challenge of abstract trademark image retrieval as a test of machine vision algorithms has attracted considerable research interest in the past decade. Current operational trademark retrieval systems involve manual annotation of the images (the current ‘gold standard’). Accordingly, current systems require a substantial amount of time and labour to access, and are therefore expensive to operate. This thesis focuses on the development of algorithms that mimic aspects of human visual perception in order to retrieve similar abstract trademark images automatically. A significant category of trademark images are typically highly stylised, comprising a collection of distinctive graphical elements that often include geometric shapes. Therefore, in order to compare the similarity of such images the principal aim of this research has been to develop a method for solving the partial matching and shape perception problem. There are few useful techniques for partial shape matching in the context of trademark retrieval, because those existing techniques tend not to support multicomponent retrieval. When this work was initiated most trademark image retrieval systems represented images by means of global features, which are not suited to solving the partial matching problem. Instead, the author has investigated the use of local image features as a means to finding similarities between trademark images that only partially match in terms of their subcomponents. During the course of this work, it has been established that the Harris and Chabat detectors could potentially perform sufficiently well to serve as the basis for local feature extraction in trademark image retrieval. Early findings in this investigation indicated that the well established SIFT (Scale Invariant Feature Transform) local features, based on the Harris detector, could potentially serve as an adequate underlying local representation for matching trademark images. There are few researchers who have used mechanisms based on human perception for trademark image retrieval, implying that the shape representations utilised in the past to solve this problem do not necessarily reflect the shapes contained in these image, as characterised by human perception. In response, a ii practical approach to trademark image retrieval by perceptual grouping has been developed based on defining meta-features that are calculated from the spatial configurations of SIFT local image features. This new technique measures certain visual properties of the appearance of images containing multiple graphical elements and supports perceptual grouping by exploiting the non-accidental properties of their configuration. Our validation experiments indicated that we were indeed able to capture and quantify the differences in the global arrangement of sub-components evident when comparing stylised images in terms of their visual appearance properties. Such visual appearance properties, measured using 17 of the proposed metafeatures, include relative sub-component proximity, similarity, rotation and symmetry. Similar work on meta-features, based on the above Gestalt proximity, similarity, and simplicity groupings of local features, had not been reported in the current computer vision literature at the time of undertaking this work. We decided to adopted relevance feedback to allow the visual appearance properties of relevant and non-relevant images returned in response to a query to be determined by example. Since limited training data is available when constructing a relevance classifier by means of user supplied relevance feedback, the intrinsically non-parametric machine learning algorithm ID3 (Iterative Dichotomiser 3) was selected to construct decision trees by means of dynamic rule induction. We believe that the above approach to capturing high-level visual concepts, encoded by means of meta-features specified by example through relevance feedback and decision tree classification, to support flexible trademark image retrieval and to be wholly novel. The retrieval performance the above system was compared with two other state-of-the-art image trademark retrieval systems: Artisan developed by Eakins (Eakins et al., 1998) and a system developed by Jiang (Jiang et al., 2006). Using relevance feedback, our system achieves higher average normalised precision than either of the systems developed by Eakins’ or Jiang. However, while our trademark image query and database set is based on an image dataset used by Eakins, we employed different numbers of images. It was not possible to access to the same query set and image database used in the evaluation of Jiang’s trademark iii image retrieval system evaluation. Despite these differences in evaluation methodology, our approach would appear to have the potential to improve retrieval effectiveness