Multiview, Few-Labeled Object Categorization by Predicting Labels With View Consistency

Learning From Weakly Labeled Data Based on Manifold Regularized Sparse Model

In multilabel learning, each training example is represented by a single instance, which is relevant to multiple class labels simultaneously. Generally, all relevant labels are considered to be available for labeled data. However, instances with a full label set are difficult to obtain in real-world applications, thus leading to the weakly multilabel learning problem, that is, relevant labels of training data are partially known and many relevant labels are missing, and even abundant training data are associated with an empty label set. To address the problem, we propose a new multilabel method to learn from weakly labeled data. To be specific, an optimization framework is constructed based on the manifold regularized sparse model, in which the correlations among labels and feature structure are considered to model global and local label correlations, thereby achieving discriminative feature analysis for mapping training data to ground-truth label space. Moreover, the proposed method has an excellent mechanism to conduct semisupervised multilabel learning by exploiting training data with the predicted label set of the unlabeled. Experiments on various real-world tasks reveal that the proposed method outperforms some state-of-the-art methods.

Exploring Misclassification Information for Fine-Grained Image Classification

Fine-grained image classification is a hot topic that has been widely studied recently. Many fine-grained image classification methods ignore misclassification information, which is important to improve classification accuracy. To make use of misclassification information, in this paper, we propose a novel fine-grained image classification method by exploring the misclassification information (FGMI) of prelearned models. For each class, we harvest the confusion information from several prelearned fine-grained image classification models. For one particular class, we select a number of classes which are likely to be misclassified with this class. The images of selected classes are then used to train classifiers. In this way, we can reduce the influence of irrelevant images to some extent. We use the misclassification information for all the classes by training a number of confusion classifiers. The outputs of these trained classifiers are combined to represent images and produce classifications. To evaluate the effectiveness of the proposed FGMI method, we conduct fine-grained classification experiments on several public image datasets. Experimental results prove the usefulness of the proposed method.

A Novel Statistical Method for Scene Classification Based on Multi-Object Categorization and Logistic Regression

In recent years, interest in scene classification of different indoor-outdoor scene images has increased due to major developments in visual sensor techniques. Scene classification has been demonstrated to be an efficient method for environmental observations but it is a challenging task considering the complexity of multiple objects in scenery images. These images include a combination of different properties and objects i.e., (color, text, and regions) and they are classified on the basis of optimal features. In this paper, an efficient multiclass objects categorization method is proposed for the indoor-outdoor scene classification of scenery images using benchmark datasets. We illustrate two improved methods, fuzzy c-mean and mean shift algorithms, which infer multiple object segmentation in complex images. Multiple object categorization is achieved through multiple kernel learning (MKL), which considers local descriptors and signatures of regions. The relations between multiple objects are then examined by intersection over union algorithm. Finally, scene classification is achieved by using Multi-class Logistic Regression (McLR). Experimental evaluation demonstrated that our scene classification method is superior compared to other conventional methods, especially when dealing with complex images. Our system should be applicable in various domains such as drone targeting, autonomous driving, Global positioning systems, robotics and tourist guide applications.

Object Categorization Processing Differs According to Category Level: Comparing Visual Information Between the Basic and Superordinate Levels

Object category levels comprise a crucial concept in the field of object recognition. Specifically, categorization performance differs according to the category level of the target object. This study involved experiments with two types of stimulus sequences (i.e., forward condition: presenting the target name before the line-drawing stimulus; and reverse condition: presenting the target name after the line-drawing stimulus) for both basic- and superordinate-level categorizations. Adult participants were assigned to each level and asked to judge whether briefly presented stimuli included the same object and target name. Here, we investigated how the category level altered the categorization process. We conducted path analyses using a multivariate multiple regression model, and set our variables to investigate whether the predictors affected the categorization process between two types of stimulus sequence. Dependent variables included the measures of performance (i.e., reaction time, accuracy) for each categorization task. The predictors included dimensions and shapes of the line-drawings, such as primary and local shape information, shape complexity, subject estimation, and other shape variables related to object recognition. Results showed that the categorization process differed according to shape properties between conditions only for basic-level categorizations. For the forward condition, the bottom-up processing of primary visual information depended on matches with stored representations for the basic-level category. For the reverse condition at the basic-level category, decisions depended on subjective ratings in terms of object-representation accessibility. Finally, superordinate-level decisions depended on higher levels of visual information in terms of complexity, regardless of the condition. Thus, the given category level altered the processing of visual information for object recognition in relation to shape properties. This indicates that decision processing for object recognition is flexible depending on the criteria of the processed objects (e.g., category levels).

Multi-View Image Classification With Visual, Semantic And View Consistency

Multi-view visual classification methods have been widely applied to use discriminative information of different views. This strategy has been proven very effective by many researchers. On the one hand, images are often treated independently without fully considering their visual and semantic correlations. On the other hand, view consistency is often ignored. To solve these problems, in this paper, we propose a novel multi-view image classification method with visual, semantic and view consistency (VSVC). For each image, we linearly combine multi-view information for image classification. The combination parameters are determined by considering both the classification loss and the visual, semantic and view consistency. Visual consistency is imposed by ensuring that visually similar images of the same view are predicted to have similar values. For semantic consistency, we impose the locality constraint that nearby images should be predicted to have the same class by multiview combination. View consistency is also used to ensure that similar images have consistent multi-view combination parameters. An alternative optimization strategy is used to learn the combination parameters. To evaluate the effectiveness of VSVC, we perform image classification experiments on several public datasets. The experimental results on these datasets show the effectiveness of the proposed VSVC method.