Image segmentation, evaluation, and applications

This thesis aims to advance research in image segmentation by developing robust techniques for evaluating image segmentation algorithms. The key contributions of this work are as follows. First, we investigate the characteristics of existing measures for supervised evaluation of automatic image segmentation algorithms. We show which of these measures is most effective at distinguishing perceptually accurate image segmentation from inaccurate segmentation. We then apply these measures to evaluating four state-of-the-art automatic image segmentation algorithms, and establish which best emulates human perceptual grouping. Second, we develop a complete framework for evaluating interactive segmentation algorithms by means of user experiments. Our system comprises evaluation measures, ground truth data, and implementation software. We validate our proposed measures by showing their correlation with perceived accuracy. We then use our framework to evaluate four popular interactive segmentation algorithms, and demonstrate their performance. Finally, acknowledging that user experiments are sometimes prohibitive in practice, we propose a method of evaluating interactive segmentation by algorithmically simulating the user interactions. We explore four strategies for this simulation, and demonstrate that the best of these produces results very similar to those from the user experiments

Toward Large Scale Semantic Image Understanding and Retrieval

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

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

Free-hand Sketch Understanding and Analysis

PhDWith the proliferation of touch screens, sketching input has become popular among many software products. This phenomenon has stimulated a new round of boom in free-hand sketch research, covering topics like sketch recognition, sketch-based image retrieval, sketch synthesis and sketch segmentation. Comparing to previous sketch works, the newly proposed works are generally employing more complicated sketches and sketches in much larger quantity, thanks to the advancements in hardware. This thesis thus demonstrates some new works on free-hand sketches, presenting novel thoughts on aforementioned topics. On sketch recognition, Eitz et al. [32] are the first explorers, who proposed the large-scale TU-Berlin sketch dataset [32] that made sketch recognition possible. Following their work, we continue to analyze the dataset and find that the visual cue sparsity and internal structural complexity are the two biggest challenges for sketch recognition. Accordingly, we propose multiple kernel learning [45] to fuse multiple visual cues and star graph representation [12] to encode the structures of the sketches. With the new schemes, we have achieved significant improvement on recognition accuracy (from 56% to 65.81%). Experimental study on sketch attributes is performed to further boost sketch recognition performance and enable novel retrieval-by-attribute applications. For sketch-based image retrieval, we start by carefully examining the existing works. After looking at the big picture of sketch-based image retrieval, we highlight that studying the sketch’s ability to distinguish intra-category object variations should be the most promising direction to proceed on, and we define it as the fine-grained sketch-based image retrieval problem. Deformable part-based model which addresses object part details and object deformations is raised to tackle this new problem, and graph matching is employed to compute the similarity between deformable part-based models by matching the parts of different models. To evaluate this new problem, we combine the TU-Berlin sketch dataset and the PASCAL VOC photo dataset [36] to form a new challenging cross-domain dataset with pairwise sketch-photo similarity ratings, and our proposed method has shown promising results on this new dataset. Regarding sketch synthesis, we focus on the generating of real free-hand style sketches for general categories, as the closest previous work [8] only managed to show efficacy on a single category: human faces. The difficulties that impede sketch synthesis to reach other categories include the cluttered edges and diverse object variations due to deformation. To address those difficulties, we propose a deformable stroke model to form the sketch synthesis into a detection process, which is directly aiming at the cluttered background and the object variations. To alleviate the training of such a model, a perceptual grouping algorithm is further proposed that utilizes stroke length’s relationship to stroke semantics, stroke temporal order and Gestalt principles [58] to perform part-level sketch segmentation. The perceptual grouping provides semantic part-level supervision automatically for the deformable stroke model training, and an iterative learning scheme is introduced to gradually refine the supervision and the model training. With the learned deformable stroke models, sketches with distinct free-hand style can be generated for many categories