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Zepeda and Pérez [41] have recently demonstrated the promise of the exemplar SVM (ESVM) as a feature encoder for image retrieval. This paper extends this approach in several directions: We first show that replacing the hinge loss by the square loss in the ESVM cost function significantly reduces encoding time with negligible effect on accuracy. We call this model square-loss exemplar machine,...
In this paper, we propose an improved image retrieval method, dedicated to images of buildings/landmarks from urban environments. Locally detected key points are binary labelled as building or no-building using a SVM-based classifier. Thereafter, only key points labelled as building are retained. In this way, the data in the database vocabulary is reduced to only the relevant one and solely the relevant...
In this paper we have proposed a color indexing scheme for image classification and retrieval using color features. Experiments were made on the Corel 1000 database, for three different color spaces, LAB, HSV and RGB. In our tests, for image classification, two discriminative classifiers, k-NN (k - Nearest Neighborhood) and SVM (Support Vector Machine) were used. Two new distances were defined and...
Discriminating the lung nodules benign or malignant is an important task for computer aided diagnosis of lung cancer. The malignancy of the nodules is divided into five levels in Lung Image Database Consortium (LIDC) database. In this study, a new content-based image retrieval (CBIR) scheme is proposed for classification of the lung nodules with different ratings. A lung nodule dataset is assembled...
This paper describes large-scale content based image retrieval system, Image Hawk search engine. ImageHawk search engine uses 23.4 million images in its gallery. Users have two different methods to make their search: Product Quantization (PQ) and Transductive Support Vector Machine based Hashing using Binary Hierarchical Trees (TSVMH-BHT). Images are first represented with 20480-dimensional Fisher...
This paper investigates different methods of representing shape and texture in content-based image retrieval. We have combined five features set in our work and these are trained and classified with SVM (support vector machine) classifier which makes use of machine learning technology. We combined histogram features, texture features (GLCM features), wavelet features, Gabor features, and statistical...
Face recognition is one of the most successful applications of image analysis and understanding and has gained much attention in last decades. In this paper, we purpose a simple and fast hybrid face recognition system based on CBIR and SVM. The Gabor wavelets (GW), Wavelet Transformation (WT), and principal component analysis (PCA) are used as feature extraction methods to generate a feature vector...
As the state-of-the-art ConvNet-based image retrieval method, spatial search has shown excellent retrieval performance and outperformed other competitors. A key component of this method is a weighted combination of distances evaluated at different regions of a query image. However, these weights are currently manually tuned, by a trial-and-error based exhaustive search. This not only incurs a lengthy...
The purpose of this paper is to describe our research and solution to the problem of designing an Encrypted Image Retrieval System. Due to rapid increase in image database volume, as well as its vast deployment in various applications, the need to secure images in the database became necessary and the need for development of efficient algorithms for proposed retrieval systems are important. The proposed...
In this research, a new technique For Content Based Image Retrieval (CBIR) with contrast enhancement using multi-feature and multi kernel Support Vector Machine (SVM) method. Color moment (CM), Auto Correlogram (AC), Discrete Wavelet Transform (DWT), Gabor Filter (GF) features are proposed. We extended the previous work which used binary SVM classifier and color features. First of all take a query...
Image retrieval is an active research area for the last two decades. This area is gaining more importance as the multimedia content over the internet is increasing. Color Texture and shape are the low level image descriptor in Content Based Image Retrieval. These low level image descriptors are used for image representation and retrieval in CBIR. This paper presents a Content Base Image Retrieval...
For more than two decades, research has been performed on content-based image retrieval (CBIR). By combining Radon projections and the support vector machines (SVM), a content-based medical image retrieval method is presented in this work. The proposed approach employs the normalized Radon projections with corresponding image category labels to build an SVM classifier, and the Radon barcode database...
An image retrieval system is a technique for browsing, searching and retrieving images from a big database of digital images. In this paper, we propose a new content-based image retrieval system that can solve the object and scene recognition problems and categorize similar images. The proposed model consists of a deep structure support vector machine with Gaussian mixture model, which is combined...
The paper proposes a mobile application for clothing coordination, which could be of great benefit for stores and people seek for fashion advices. The application matches apparel image input with, previously saved apparel images, and then provides the user with the possible matching suggestions based on the apparel outline and dominating colors. For this purpose two Region of Interest (ROI) extraction...
To narrow down the semantic gap and increase the retrieval efficiency in image retrieval, relevance feedback (RF) has long been an important approach, where the active support vector machine (SVM) based RFs are widely applied to content-based image retrieval (CBIR). However, the performance of these methods are often poor because the low speed of SVM algorithm in high dimension data. Meanwhile, the...
In this paper, we propose an Active Learning approach to query by example retrieval, using a retraining procedure that improves the understanding of the machine with respect to the human perception. The proposed method is based on Support Vector Machine (SVM) classifiers and requires a small number of training samples. The classifier is retrained several times in order to determine the optimal separating...
This study is trying to assess methods commonly used in content-based image retrieval (CBIR) for screening mammography analysis. A database consists of 20 different BI-RADS classes of mammogram patches taken from IRMA database is used in this study. Three feature extraction methods, namely grey-level co-occurrence matrix(GLCM), principal component analysis, and scale-invariant feature transform (SIFT)...
In this paper, we discuss some of the key contributions in the current decade related to image retrieval and automated image annotation. General content-based image retrieval (CBIR) also could be improved by the proposed approach in a similar manner as text-based retrieval is improved. In this case no text information is available, but only visual features are used. The CBIR identifies relevant articles...
Content Based Image Retrieval (CBIR) is a developing trend in Digital Image Processing for searching and retrieving the query image from wide range of databases. Conventional content-based image retrieval (CBIR) schemes have following limitations: 1. It is slow 2. difficult to label negative examples; 3. Accuracy is poor in a single step; 4. users may introduce some noisy examples into the query....
RGIRS (Remote Geo-system Image Retrieval System) is a system of retrieving similar image using image features like color feature, texture feature and shape feature. Content based image retrieval system extracts features relevant to query image using feature extraction method. Many RGIRS systems are proposed to retrieve accurate similar image but the problem is no method provides accurate results....
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