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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...
We address the problem of retrieving the silhouettes of objects from a database of shapes with a translation and rotation invariant feature extractor. We retrieve silhouettes by using a “soft” classification based on the Euclidean distance. Experiments show significant gains in retrieval accuracy over the existing literature. This work extends the use of our previously employed feature extractor and...
Color and texture are visual cues of different nature, their integration in a useful visual descriptor is not an obvious step. One way to combine both features is to compute texture descriptors independently on each color channel. A second way is integrate the features at a descriptor level, in this case arises the problem of normalizing both cues. A significant progress in the last years in object...
Existence of countless digital images has given rise to image retrieval in many applications. Conventional image databases being text-annotated pose two major problems of keywords for images and complexity. Hence, retrieval systems based on image's visual content are more desirable [1]. The content based image retrieval (CBIR) technique, employed here uses visual cues to retrieve images. This technique...
High precision identification of feature points is an important technology and one of the bases of computer vision, image analysis and image processing. In the practical applications, the feature points can not be identified easily in various conditions of light illumination, visual angle, texture, and perspective projection. And in many cases, the size of feature point is small, the context is uncertain...
The main goal of multiscale analysis of visual texture is to extract the same texture descriptors from two images at different scales, but most of the methodologies obtain different texture descriptors. The effect of support area selection on the Local Binary Pattern (LBP) operators is discussed here for scale invariant texture descriptor extraction. In this work, determination of the fundamental...
In order to improve the average recall rate and the average precision rate of image retrieval, an improved fusion algorithm of the weighted features is presented. Firstly,the shape features of images are extracted by using the moment invariant method based on 7 central moments. Meanwhile,the texture features of images are calculated by using the Gray-level Co-occurrence matrix. Then the elements of...
In this work, we described a new two-stage hierarchical framework for mammogram retrieval. We tested the proposed approach on the reference library from USF-DDSM. For each query ROI (region of interest), the proposed scheme first computes its 14 texture and shape features, then the voting method based on five classifiers is used to classify the ROIs in the reference library, this phase eliminates...
Our goal is to organize the image contents semantically. In this paper, we propose a method to classify the images semantically, using the C-fuzzy algorithm to segment the natural scenes into perceptually uniform regions. The low-level characteristics that are taken into account are: color, texture, shape, absolute spatial arrangement, spatial coherency, and dimension. Since humans are the ultimate...
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