64 research outputs found

    A mutual GrabCut method to solve co-segmentation

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    Extent: 11 p.Co-segmentation aims at segmenting common objects from a group of images. Markov random field (MRF) has been widely used to solve co-segmentation, which introduces a global constraint to make the foreground similar to each other. However, it is difficult to minimize the new model. In this paper, we propose a new Markov random field-based co-segmentation model to solve co-segmentation problem without minimization problem. In our model, foreground similarity constraint is added into the unary term of MRF model rather than the global term, which can be minimized by graph cut method. In the model, a new energy function is designed by considering both the foreground similarity and the background consistency. Then, a mutual optimization approach is used to minimize the energy function. We test the proposed method on many pairs of images. The experimental results demonstrate the effectiveness of the proposed method.Zhisheng Gao, Peng Shi, Hamid Reza Karimi and Zheng Pe

    Global optimisation techniques for image segmentation with higher order models

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    Energy minimisation methods are one of the most successful approaches to image segmentation. Typically used energy functions are limited to pairwise interactions due to the increased complexity when working with higher-order functions. However, some important assumptions about objects are not translatable to pairwise interactions. The goal of this thesis is to explore higher order models for segmentation that are applicable to a wide range of objects. We consider: (1) a connectivity constraint, (2) a joint model over the segmentation and the appearance, and (3) a model for segmenting the same object in multiple images. We start by investigating a connectivity prior, which is a natural assumption about objects. We show how this prior can be formulated in the energy minimisation framework and explore the complexity of the underlying optimisation problem, introducing two different algorithms for optimisation. This connectivity prior is useful to overcome the “shrinking bias” of the pairwise model, in particular in interactive segmentation systems. Secondly, we consider an existing model that treats the appearance of the image segments as variables. We show how to globally optimise this model using a Dual Decomposition technique and show that this optimisation method outperforms existing ones. Finally, we explore the current limits of the energy minimisation framework. We consider the cosegmentation task and show that a preference for object-like segmentations is an important addition to cosegmentation. This preference is, however, not easily encoded in the energy minimisation framework. Instead, we use a practical proposal generation approach that allows not only the inclusion of a preference for object-like segmentations, but also to learn the similarity measure needed to define the cosegmentation task. We conclude that higher order models are useful for different object segmentation tasks. We show how some of these models can be formulated in the energy minimisation framework. Furthermore, we introduce global optimisation methods for these energies and make extensive use of the Dual Decomposition optimisation approach that proves to be suitable for this type of models

    Design Simulation and Assessment of Cellular Automata Based Improved Image Segmentation System

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    A variety of methods may be found in the numerous image segmentation techniques. Here a method of text retrieval conducted is typically to produce a collection of localized features. In computer science, object recognition is the problem of automatically "identifying", or classifying, an object. In certain instances, the awareness of artifacts is deeper into image in image segmentation through image processing. The algorithm used for image segmentation has a direct impact on the outcome of the whole approach, therefore it is important to choose carefully. It is important to choose a segmentation method appropriate for a certain framework. There are several ready-to-use segmentation methods, so one by one evaluate the tools to see which works best. Segmentation algorithms have reached such a level of complexity that a research employing them is often considered impractical. The given research undertakes the process of improved graph cut method to select the foreground and background of image through labelling and segmentation of the image. Results have been compared on the performance parameter to analyse the effectiveness of the proposed algorithm for segmentation of the images

    Toward Large Scale Semantic Image Understanding and Retrieval

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    Semantic image retrieval is a multifaceted, highly complex problem. Not only does the solution to this problem require advanced image processing and computer vision techniques, but it also requires knowledge beyond what can be inferred from the image content alone. In contrast, traditional image retrieval systems are based upon keyword searches on filenames or metadata tags, e.g. Google image search, Flickr search, etc. These conventional systems do not analyze the image content and their keywords are not guaranteed to represent the image. Thus, there is significant need for a semantic image retrieval system that can analyze and retrieve images based upon the content and relationships that exist in the real world.In this thesis, I present a framework that moves towards advancing semantic image retrieval in large scale datasets. At a conceptual level, semantic image retrieval requires the following steps: viewing an image, understanding the content of the image, indexing the important aspects of the image, connecting the image concepts to the real world, and finally retrieving the images based upon the index concepts or related concepts. My proposed framework addresses each of these components in my ultimate goal of improving image retrieval. The first task is the essential task of understanding the content of an image. Unfortunately, typically the only data used by a computer algorithm when analyzing images is the low-level pixel data. But, to achieve human level comprehension, a machine must overcome the semantic gap, or disparity that exists between the image data and human understanding. This translation of the low-level information into a high-level representation is an extremely difficult problem that requires more than the image pixel information. I describe my solution to this problem through the use of an online knowledge acquisition and storage system. This system utilizes the extensible, visual, and interactable properties of Scalable Vector Graphics (SVG) combined with online crowd sourcing tools to collect high level knowledge about visual content.I further describe the utilization of knowledge and semantic data for image understanding. Specifically, I seek to incorporate knowledge in various algorithms that cannot be inferred from the image pixels alone. This information comes from related images or structured data (in the form of hierarchies and ontologies) to improve the performance of object detection and image segmentation tasks. These understanding tasks are crucial intermediate steps towards retrieval and semantic understanding. However, the typical object detection and segmentation tasks requires an abundance of training data for machine learning algorithms. The prior training information provides information on what patterns and visual features the algorithm should be looking for when processing an image. In contrast, my algorithm utilizes related semantic images to extract the visual properties of an object and also to decrease the search space of my detection algorithm. Furthermore, I demonstrate the use of related images in the image segmentation process. Again, without the use of prior training data, I present a method for foreground object segmentation by finding the shared area that exists in a set of images. I demonstrate the effectiveness of my method on structured image datasets that have defined relationships between classes i.e. parent-child, or sibling classes.Finally, I introduce my framework for semantic image retrieval. I enhance the proposed knowledge acquisition and image understanding techniques with semantic knowledge through linked data and web semantic languages. This is an essential step in semantic image retrieval. For example, a car class classified by an image processing algorithm not enhanced by external knowledge would have no idea that a car is a type of vehicle which would also be highly related to a truck and less related to other transportation methods like a train . However, a query for modes of human transportation should return all of the mentioned classes. Thus, I demonstrate how to integrate information from both image processing algorithms and semantic knowledge bases to perform interesting queries that would otherwise be impossible. The key component of this system is a novel property reasoner that is able to translate low level image features into semantically relevant object properties. I use a combination of XML based languages such as SVG, RDF, and OWL in order to link to existing ontologies available on the web. My experiments demonstrate an efficient data collection framework and novel utilization of semantic data for image analysis and retrieval on datasets of people and landmarks collected from sources such as IMDB and Flickr. Ultimately, my thesis presents improvements to the state of the art in visual knowledge representation/acquisition and computer vision algorithms such as detection and segmentation toward the goal of enhanced semantic image retrieval
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