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The performance of pattern classifiers depends on the separability of the classes in the feature space — a property related to the quality of the descriptors — and the choice of informative training samples for user labeling — a procedure that usually requires active learning. This work is devoted to improve the quality of the descriptors when samples are superpixels from remote sensing images. We...
Classification of digital images into photographs and various kinds of non-photographic images has not been sufficiently studied but has many applications such as retrieval of real scene photographs from web sites and image databases. In this paper, we show that the combination of Bag of Visual Words of SURF features and histograms of LBPs for HSV and Luminance components (SURF+LBP(HSVL)) is simple,...
Whole slide imaging technology enables pathologists to screen biopsy images and make a diagnosis in a digital form. This creates an opportunity to understand the screening patterns of expert pathologists and extract the patterns that lead to accurate and efficient diagnoses. For this purpose, we are taking the first step to interpret the recorded actions of world-class expert pathologists on a set...
Wireless capsule endoscopy (WCE) enables non-invasive visual inspection of the patients' digestive tract. However, the huge number of images from the WCE has been a hurdle for doctors to handle and thus it is urgent to develop computer-aided diagnosis systems to identify problematic images. To tackle this problem, an innovative algorithm based on the integration of the Bag of Features (BoF) method...
In this paper, we propose a novel approach based on two ideas, one using the trajectory extracted from videos as the local descriptors, and, second, using a similarity constrained latent support vector machine approach, to enforce the consistency of the selection of regions of interest generated by such trajectory based local extractors. We compare the performance of the proposed approach with other...
Young children need their parents' love and care but their parents are not always available to tell them stories. This motivates our proposal of a Smart Teddy Bear, a vision-based story teller, to assist young children in their social-emotional growth. When a young child wants to listen to a story, he or she simply opens any page in that book before a Smart Teddy Bear and the system automatically...
The recognition of vehicle manufacturer logo is a crucial and very challenging problem, which is still an area with few published effective methods. This paper proposes a new fast and reliable system for Vehicle Logo Recognition (VLR) based on Bag-of-Words (BoW). In our system, vehicle logo images are represented as histograms of visual words and classified by SVM in three steps: firstly, extract...
One of the main important parts in the robot vision system of the mushroom harvesting robot is to detect mushroom damage either caused by microbial or mechanical origin. Mushrooms must be classified as healthy or unhealthy to ensure proper handling and maximize crop yield. To solve the problem of identification, a fast and non-destructive method, Support Vector Machine (SVM), is applied to improve...
Human action classification is an important task in computer vision. The Bag-of-Words model uses spatio-temporal features assigned to visual words of a vocabulary and some classification algorithm to attain this goal. In this work we have studied the effect of reducing the vocabulary size using a video word ranking method. We have applied this method to the KTH dataset to obtain a vocabulary with...
This paper studies a method for learning a discriminative visual codebook for various computer vision tasks such as image categorization and object recognition. The performance of various computer vision tasks depends on the construction of the codebook which is a table of visual-words (i.e. codewords). This paper proposed a learning criterion for constructing a discriminative codebook, and it is...
In this paper we propose a novel approach for introducing semantic relations into the bag-of-words framework for recognizing human actions. We represent visual words in two different views: the original features and the document co-occurrence representation. The latter view conveys semantic relations but is large, sparse and noisy. We use canonical correlation analysis between the two views to find...
Traditional approaches in object class recognition utilize a large number of labeled visual examples in order to train classifiers to recognize the category of an object in a test image. However, the need for a large number of training data makes the scalability of this approach problematic. In this paper, we explore the recently proposed paradigm of attribute based category recognition for object...
In scene categorization, one single histogram based on the sole universal codebook is used to characterize an image in most state-of-the-art scene categorization methods, which is lack of enough discriminative ability to separate the images among different categories and results in low classification accuracy. In order to solve the problem, in this paper, we propose a novel scene categorization approach...
In recent years, scene classification based on local correlation of binarized projection lengths in subspace obtained by Kernel Principal Component Analysis (KPCA) of visual words was proposed and its effectiveness was shown. However, local correlation of 2 binary features becomes 1 only when both features are 1. In other cases, local correlation becomes 0. This discarded information. In this paper,...
One commonly used approach to scene localization and landmark recognition is to match an input image against a large annotated database of images using local image features. However problems exist with these approaches relating to memory constraints and the processing time required to compare high dimensional image feature vectors in a very large scale database. We investigate a new landmark classification...
Most recent methods for image classification focus on how to formulate different types of features effectively in a uniform formula. Although these features take on different importance for image classification, most previous work gives the same weight to the features when they are combined. In this paper, we propose an approach to integrate multi-features by following the multiple kernel learning...
Common visual codebook generation methods used in a Bag of Visual words model, e.g. k-means or Gaussian Mixture Model, use the Euclidean distance to cluster features into visual code words. However, most popular visual descriptors are histograms of image measurements. It has been shown that the Histogram Intersection Kernel (HIK) is more effective than the Euclidean distance in supervised learning...
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