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Traditional content-based image classifications often fail to meet a user's need due to the `semantic gap' between the texture features and the semantic features of the image. Content-based indexing and retrieval of images requires a proper semantic description for image content. This paper presents a novel approach based on semantic features and Bayesian networks for image classification. A mapping...
Scene classification from images is a challenging problem in computer vision due to its significant variability of scale, illumination, and view. Recently, Latent Dirichlet Allocation (LDA) model has grown popular in computer vision field, especially in scene labeling and classification. However, the effectiveness of the LDA model for the scene classification has not yet been addressed thoroughly...
Automatic classification of cancer lesions for gastroenterology imaging scenarios poses novel challenges to computer assisted decision systems, owing to their distinct visual characteristics such as reduced color spaces or natural organic textures. In this paper, we explore the prospects of using Gabor filters in a texton framework for the classification of images from two distinct imaging modalities...
Decoding perceptual or cognitive states based on brain activity measured using functional Magnetic Resonance Imaging (fMRI) can be achieved using machine learning algorithms to train classifiers of specific stimuli. However, the high dimensionality and intrinsically low Signal-to-Noise Ratio (SNR) of fMRI data poses great challenges to such techniques. The problem is aggravated in the case of multiple...
In recent years, a content-based method such as `bag-of-features' (BoF) is coming to the fore as an object recognition and classification technique. This paper proposes a BoF signature using invariant region descriptor for object retrieval. The region descriptors are extracted from dense sampled regions in the training images. These descriptors are quantized by hierarchical k-means clustering in a...
Video search today uses the metadata surrounding the video, ignoring its semantic content. Over the years, a lot of research has gone into indexing and browsing of sports video content. In this work, we present a novel approach for classification of events in cricket videos and thus, summarize its visual content. The proposed method segments a cricket video into shots and identifies the visual content...
We propose a novel approach for enhancing precision in a leading video analytics system that detects cashier fraud in grocery stores for loss prevention. While intelligent video analytics has recently become a promising means of loss prevention for retailers, most of the real-world systems suffer from a large number of false alarms, resulting in a significant waste of human labor during manual verification...
Automatic annotation of images is a challenging task in computer vision because of “semantic gap” between highlevel visual concepts and image appearances. Therefore, user tags attached to images can provide further information to bridge the gap, even though they are partially uninformative and misleading. In this work, we investigate multi-modal visual concept classification based on visual features...
Natural image classification is an important task. SIFT descriptors and bag-of-visterms (BOV) method have achieved very good results based on local image representation. Many studies use the support vector machine to classify and identify the image category after finished representation of the image. However, due to support vector machine (SVM) its own characteristics, it shows inflexible and less...
Automated visual inspection system (AVIS) is a method of analyzing, classifying, detection defects for products at the production line. Usually, this inspection is either conducted by human, machine or both. In this paper, we explain an algorithm that capable to classify mechanical products in real time. The system is consists of two parts: hardware and software. The algorithm used the web-camera...
Object recognition systems need effective image descriptors to obtain good performance levels. Currently, the most widely used image descriptor is the SIFT descriptor that computes histograms of orientation gradients around points in an image. A possible problem of this approach is that the number of features becomes very large when a dense grid is used where the histograms are computed and combined...
In this paper, we address the problem of recognizing object categories by proposing a learning model based on evolutionary algorithm that takes unsegmented, complex images which is tolerant to 2D affine transformations such as scaling and translation in the image plane and 3D transformations of an object such as illumination changes and rotation in depth. To achieve this, first object features are...
This paper presents a novel architecture for a classification system based on the visual saliency of images. The work is motivated by the difficulty of reviewing large numbers of images as a human operator in the context of Autonomous Underwater Vehicle (AUV) surveys. We formulate a feature space in which an algorithm operates over color and texture to determine saliency and illustrate how this can...
The bag of visual words model has seen immense success in addressing the problem of image classification. Algorithms using this model generate the codebook of visual words by vector quantizing the features (such as SIFT) of the images to be classified. However, a codebook so formed tends to get biased by the nature of the dataset. In this paper we propose an alternative method to create the codebook...
This paper describes a vision-based ground-plane classification system for autonomous indoor mobile-robot that takes advantage of the synergy in combining together multiple visual-cues. A priori knowledge of the environment is important in many biological systems, in parallel with their reactive systems. As such, a learning model approach is taken here for the classification of the ground/object space,...
Enterprise content repositories often consist of business documents comprising not only of traditional text data but also graphics (org charts, graphs, architecture diagrams, etc.) that get reused by people across the enterprise. Despite this diversity of content, most of the research in enterprise search has focused on improving document search. We describe a machine learning approach for graphics...
Nuclear proliferation is a major national security concern for many countries. Existing feature extraction and classification approaches are not suitable for monitoring proliferation activity using high-resolution multi-temporal remote sensing imagery. In this paper we present an unsupervised semantic labeling framework based on the Latent Dirichlet Allocation method. This framework is used to analyze...
Feature enhancement in an image is to reinforce some exacted features so that it can be used for object classification and detection. As the thermal image is lack of texture and colorful information, the techniques for visual image feature enhancement is insufficient to apply to thermal images. In this paper, we propose a new gradient-based approach for feature enhancement in thermal image. We use...
This paper presents a new model for capturing spatial information for object categorization with bag-of-words (BOW). BOW models have recently become popular for the task of object recognition, owing to their good performance and simplicity. Much work has been proposed over the years to improve the BOW model, where the Spatial Pyramid Matching (SPM) technique is the most notable. We propose a new method...
In this paper, we designed a Computer-Aided-Diagnosis (CAD) system for lesion detection in breast MR images. The CAD process begins with analysis of MR images to detect the existence of lesion. If lesion exists, it is then coloured based on its type; benign, suspicious or malignant. Our CAD system enables better visualization of the lesions and improves accuracy as well as speed for breast cancer...
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