Analysis of distance metrics in content-based image retrieval using statistical quantized histogram texture features in the DCT domain

AbstractThe effective content-based image retrieval (CBIR) needs efficient extraction of low level features like color, texture and shapes for indexing and fast query image matching with indexed images for the retrieval of similar images. Features are extracted from images in pixel and compressed domains. However, now most of the existing images are in compressed formats like JPEG using DCT (discrete cosine transformation). In this paper we study the issues of efficient extraction of features and the effective matching of images in the compressed domain. In our method the quantized histogram statistical texture features are extracted from the DCT blocks of the image using the significant energy of the DC and the first three AC coefficients of the blocks. For the effective matching of the image with images, various distance metrics are used to measure similarities using texture features. The analysis of the effective CBIR is performed on the basis of various distance metrics in different number of quantization bins. The proposed method is tested by using Corel image database and the experimental results show that our method has robust image retrieval for various distance metrics with different histogram quantization in a compressed domain

CONTENT-BASED IMAGE RETRIEVAL USING ENHANCED HYBRID METHODS WITH COLOR AND TEXTURE FEATURES

Content-based image retrieval (CBIR) automatically retrieves similar images to the query image by using the visual contents (features) of the image like color, texture and shape. Effective CBIR is based on efficient feature extraction for indexing and on effective query image matching with the indexed images for retrieval. However the main issue in CBIR is that how to extract the features efficiently because the efficient features describe well the image and they are used efficiently in matching of the images to get robust retrieval. This issue is the main inspiration for this thesis to develop a hybrid CBIR with high performance in the spatial and frequency domains. We propose various approaches, in which different techniques are fused to extract the statistical color and texture features efficiently in both domains. In spatial domain, the statistical color histogram features are computed using the pixel distribution of the Laplacian filtered sharpened images based on the different quantization schemes. However color histogram does not provide the spatial information. The solution is by using the histogram refinement method in which the statistical features of the regions in histogram bins of the filtered image are extracted but it leads to high computational cost, which is reduced by dividing the image into the sub-blocks of different sizes, to extract the color and texture features. To improve further the performance, color and texture features are combined using sub-blocks due to the less computational cos

An analysis of a discrete cosine transform (DCT)compression technique on low level features of image for image retrieval system

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The aceToolbox: low-level audiovisual feature extraction for retrieval and classification

In this paper we present an overview of a software platform that has been developed within the aceMedia project, termed the aceToolbox, that provides global and local lowlevel feature extraction from audio-visual content. The toolbox is based on the MPEG-7 eXperimental Model (XM), with extensions to provide descriptor extraction from arbitrarily shaped image segments, thereby supporting local descriptors reflecting real image content. We describe the architecture of the toolbox as well as providing an overview of the descriptors supported to date. We also briefly describe the segmentation algorithm provided. We then demonstrate the usefulness of the toolbox in the context of two different content processing scenarios: similarity-based retrieval in large collections and scene-level classification of still images

CONTENT-BASED IMAGE RETRIEVAL USING ENHANCED HYBRID METHODS WITH COLOR AND TEXTURE FEATURES

Content-based image retrieval (CBIR) automatically retrieves similar images to the query image by using the visual contents (features) of the image like color, texture and shape. Effective CBIR is based on efficient feature extraction for indexing and on effective query image matching with the indexed images for retrieval. However the main issue in CBIR is that how to extract the features efficiently because the efficient features describe well the image and they are used efficiently in matching of the images to get robust retrieval. This issue is the main inspiration for this thesis to develop a hybrid CBIR with high performance in the spatial and frequency domains. We propose various approaches, in which different techniques are fused to extract the statistical color and texture features efficiently in both domains. In spatial domain, the statistical color histogram features are computed using the pixel distribution of the Laplacian filtered sharpened images based on the different quantization schemes. However color histogram does not provide the spatial information. The solution is by using the histogram refinement method in which the statistical features of the regions in histogram bins of the filtered image are extracted but it leads to high computational cost, which is reduced by dividing the image into the sub-blocks of different sizes, to extract the color and texture features. To improve further the performance, color and texture features are combined using sub-blocks due to the less computational cos

A NEW DESCRIPTOR BASED ON 2D DCT FOR IMAGE RETRIEVAL

International audienceContent-based image retrieval relies on feature comparison between images. So the selection of feature vector is important. As many images are compressed by transforms, constructing the feature vector directly in transform domain is a very popular topic. We propose a new feature vector in DCT domain. Our method selects part of DCT coefficients inside each block to construct AC-Pattern and use DC coefficients between neighboring blocks to construct DC-Pattern. Two histograms are formed and parts of them are used to build a descriptor vector integrating features to do image retrieval. Experiments are done both on face image databases and texture image database. Compared to other methods, results show that we can get better performance on both face and texture database by using the proposed method

The Effective of Image Retrieval in Jpeg Compressed Domain

We propose a new method of feature extraction in orderto improve the effective of image retrieving by using apartial Joint Photographic Experts Group (JPEG)compressed images algorithm. Prior to that, we prune theimages database by pre-query step based on coloursimilarity, in order to eliminate image candidates. Ourfeature extraction can be carried out directly to JPEGcompressed images. We extract two features of DCTcoefficients, DC feature and AC feature, from a JPEGcompressed image. Then we compute the Euclideandistances between the query image and the images in adatabase in terms of these two features. The image querysystem will give each retrieved image a rank to define itssimilarity to the query image. Moreover, instead of fullydecompressing JPEG images, our system only needs to dopartial entropy decoding. Therefore, our proposed schemecan accelerate the effectiveness of retrieving images.According to our experimental results, our system is notonly highly effective but is also capable of performingsatisfactoril

Segmentation for Image Indexing and Retrieval on Discrete Cosines Domain

This paper used region growing segmentation technique to segment the Discrete Cosines (DC) image. The classic problem of content Based image retrieval (CBIR) is the lack of accuracy in matching between image query and image in the database. By using region growing technique on DC image,it reduced the number of image regions indexed. The proposed of recursive region growing is not new technique but its application on DC images to build  indexing keys is quite new and not yet presented by many  authors. The experimental results show that the proposed methods on segmented images present good precision which are higher than 0.60 on all classes. So, it could be concluded that region growing segmented based CBIR more efficient   compared to DC images in term of their precision 0.59 and 0.75, respectively. Moreover, DC based CBIR can save time and simplify algorithm compared to DCT images. The most significant finding from this work is instead of using 64 DCT coefficients this research only used 1/64 coefficients which is DC coefficient.