Fusion Of Global Shape And Local Features Using Multi Classifier Framework For Object Class Recognition

Object class recognition deals with the classification of individual objects to a certain class. In images of natural scenes, objects appear in a variety of poses and scales, with or without occlusion. Object class recognition typically involves the extraction, processing and analysis of visual features such as color, shape, or texture from an object, and then associating a class label to it. In this thesis, global shape and local features are considered as discriminative features for object class recognition. For local features, misclassification problems occur if the object is too small and possess weak local features. Besides that, local features do not give implicit importance to the shape of the object, which is one of important features to human vision. Detecting objects is difficult if the pose changes. Consequently, pose changes will result in changes in shape features for an object in the same class. Hence, both local and shape features are combined in order to obtain better classification performance for each object class. Ultimately, a meta-classifier framework is proposed as a model for object class recognition. Meta-classifier is used to learn a meta-classifier that optimally predicts the correctness of classification of base classifier for each object. In this framework, individual classifiers are trained using the local and global shape features, respectively. Then, these classifiers results are combined as input to the meta-classifier. Experimental results have shown to be comparable, or superior to existing state-of- the-art works for object class recognition

Complex Feature Recognition: A Bayesian Approach for Learning to Recognize Objects

We have developed a new Bayesian framework for visual object recognition which is based on the insight that images of objects can be modeled as a conjunction of local features. This framework can be used to both derive an object recognition algorithm and an algorithm for learning the features themselves. The overall approach, called complex feature recognition or CFR, is unique for several reasons: it is broadly applicable to a wide range of object types, it makes constructing object models easy, it is capable of identifying either the class or the identity of an object, and it is computationally efficient--requiring time proportional to the size of the image. Instead of a single simple feature such as an edge, CFR uses a large set of complex features that are learned from experience with model objects. The response of a single complex feature contains much more class information than does a single edge. This significantly reduces the number of possible correspondences between the model and the image. In addition, CFR takes advantage of a type of image processing called 'oriented energy'. Oriented energy is used to efficiently pre-process the image to eliminate some of the difficulties associated with changes in lighting and pose

Interactive Perception Based on Gaussian Process Classification for House-Hold Objects Recognition and Sorting

We present an interactive perception model for object sorting based on Gaussian Process (GP) classification that is capable of recognizing objects categories from point cloud data. In our approach, FPFH features are extracted from point clouds to describe the local 3D shape of objects and a Bag-of-Words coding method is used to obtain an object-level vocabulary representation. Multi-class Gaussian Process classification is employed to provide and probable estimation of the identity of the object and serves a key role in the interactive perception cycle – modelling perception confidence. We show results from simulated input data on both SVM and GP based multi-class classifiers to validate the recognition accuracy of our proposed perception model. Our results demonstrate that by using a GP-based classifier, we obtain true positive classification rates of up to 80%. Our semi-autonomous object sorting experiments show that the proposed GP based interactive sorting approach outperforms random sorting by up to 30% when applied to scenes comprising configurations of household objects

Feature fusion, feature selection and local n-ary patterns for object recognition and image classification

University of Technology Sydney. Faculty of Engineering and Information Technology.Object recognition is one of the most fundamental topics in computer vision. During past years, it has been the interest for both academies working in computer science and professionals working in the information technology (IT) industry. The popularity of object recognition has been proven by its motivation of sophisticated theories in science and wide spread applications in the industry. Nowadays, with more powerful machine learning tools (both hardware and software) and the huge amount of information (data) readily available, higher expectations are imposed on object recognition. At its early stage in the 1990s, the task of object recognition can be as simple as to differentiate between object of interest and non-object of interest from a single still image. Currently, the task of object recognition may as well includes the segmentation and labeling of different image regions (i.e., to assign each segmented image region a meaningful label based on objects appear in those regions), and then using computer programs to infer the scene of the overall image based on those segmented regions. The original two-class classification problem is now getting more complex as it now evolves toward a multi-class classification problem. In this thesis, contributions on object recognition are made in two aspects. These are, improvements using feature fusion and improvements using feature selection. Three examples are given in this thesis to illustrate three different feature fusion methods, the descriptor concatenation (the low-level fusion), the confidence value escalation (the mid-level fusion) and the coarse-to-fine framework (the high-level fusion). Two examples are provided for feature selection to demonstrate its ideas, those are, optimal descriptor selection and improved classifier selection. Feature extraction plays a key role in object recognition because it is the first and also the most important step. If we consider the overall object recognition process, machine learning tools are to serve the purpose of finding distinctive features from the visual data. Given distinctive features, object recognition is readily available (e.g., a simple threshold function can be used to classify feature descriptors). The proposal of Local N-ary Pattern (LNP) texture features contributes to both feature extraction and texture classification. The distinctive LNP feature generalizes the texture feature extraction process and improves texture classification. Concretely, the local binary pattern (LBP) is the special case of LNP with n = 2 and the texture spectrum is the special case of LNP with n = 3. The proposed LNP representation has been proven to outperform the popular LBP and one of the LBP’s most successful extension - local ternary pattern (LTP) for texture classification

Probabilistic Integration of Intensity and Depth Information for Part-Based Vehicle Detection

International audienceIn this paper, an object class recognition method is presented. The method uses local image features and follows the part-based detection approach. It fuses intensity and depth information in a probabilistic framework. The depth of each local feature is used to weigh the probability of finding the object at a given distance. To train the system for an object class, only a database of images annotated with bounding boxes is required, thus automatizing the extension of the system to different object classes. We apply our method to the problem of detecting vehicles from a moving platform. The experiments with a data set of stereo images in an urban environment show a significant improvement in performance when using both information modalities

Local, Semi-Local and Global Models for Texture, Object and Scene Recognition

This dissertation addresses the problems of recognizing textures, objects, and scenes in photographs. We present approaches to these recognition tasks that combine salient local image features with spatial relations and effective discriminative learning techniques. First, we introduce a bag of features image model for recognizing textured surfaces under a wide range of transformations, including viewpoint changes and non-rigid deformations. We present results of a large-scale comparative evaluation indicating that bags of features can be effective not only for texture, but also for object categization, even in the presence of substantial clutter and intra-class variation. We also show how to augment the purely local image representation with statistical co-occurrence relations between pairs of nearby features, and develop a learning and classification framework for the task of classifying individual features in a multi-texture image. Next, we present a more structured alternative to bags of features for object recognition, namely, an image representation based on semi-local parts, or groups of features characterized by stable appearance and geometric layout. Semi-local parts are automatically learned from small sets of unsegmented, cluttered images. Finally, we present a global method for recognizing scene categories that works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting spatial pyramid representation demonstrates significantly improved performance on challenging scene categorization tasks

Fusion of global shape and local features using meta-classifier framework.

In computer vision, objects in an image can be described using many features such as shape, color, texture and local features. The number of dimensions for each type of feature has differing size. Basically, the underlying belief from a recognition point of view is that, the more features being used, the better the recognition performance. However, having more features does not necessarily correlate to better performance. The higher dimensional vectors resulting from fusion might contain irrelevant or noisy features that can degrade classifier performance. Repetitive and potentially useless information might be present which further escalates the 'curse of dimensionality' problem. Consequently, unwanted and irrelevant features are removed from the combination of features. Although this technique provides promising recognition performance, it is not efficient when it comes to computational time in model building. This study proposes meta- classifier framework to ensure all relevant features are not ignored, while maintaining minimal computational time. In this framework, individual classifiers are trained using the local and global shape features, respectively. Then, these classifiers results are combined as input to the meta- classifier. Experimental results have shown to be comparable, or superior to existing state-of-the-art works for object class recognition