Cbir

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  • Topic: Content-based image retrieval, Color, Color space
  • Pages : 5 (1047 words )
  • Download(s) : 68
  • Published : March 19, 2013
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CONTENT BASED IMAGE RETRIEVAL USING COLOUR AND TEXTURE FEATURE EXTRACTION

Abstract

We propose a content-based image retrieval method based on an efficient combination of multiresolution color and texture features. As its color features, color auto correlograms of the hue and saturation component images in HSV color space are used. As its texture features, BDIP and BVLC moments of the value component image are adopted. The color and texture features are extracted in multiresolution wavelet domain and combined. The dimension of the combined feature vector is determined at a point where the retrieval accuracy becomes saturated. Experimental results show that the proposed method yields higher retrieval accuracy than some conventional methods even though its feature vector dimension is not higher than those of the latter for six test DBs. Especially, it demonstrates more excellent retrieval accuracy for queries and target images of various resolutions. In addition, the proposed method almost always shows performance gain in precision versus recall and in ANMRR over the other methods.

Introduction

CONTENT BASED IMAGE RETRIEVAL

"Content-based" means that the search will analyze the actual contents of the image rather than the metadata such as keywords, tags, and/or descriptions associated with the image. The term 'content' in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. CBIR is desirable because most web based image search engines rely purely on metadata and this produces a lot of garbage in the results. Also having humans manually enter keywords for images in a large database can be inefficient, expensive and may not capture every keyword that describes the image. Thus a system that can filter images based on their content would provide better indexing and return more accurate results.

1.1 CBIR USING COLOUR AND TEXTURE

Color is one of the most widely used visual features and is invariant to image size and orientation . As conventional color features used in CBIR, there are color histogram, color correlogram , color structure descriptor (CSD), and scalable color descriptor (SCD). The latter two are MPEG-7 color descriptors. Color histogram is the most commonly used color representation, but it does not include any spatial information. On the other hand, color correlogram describes the probability of finding color pairs at a fixed pixel distance and provides spatial information. Therefore color correlogram yields better retrieval accuracy in comparison to color histogram . Color autocorrelogram is a subset of color correlogram, which captures the spatial correlation between identical colors only. Since it provides significant computational benefits over color correlogram , it is more suitable for image retrieval.

Texture is also a visual feature that refers to innate surface properties of an object and their relationship to the surrounding environment . In conventional texture features used for CBIR, there are statistic texture features using gray-level co-occurrence matrix (GLCM) , edge histogram descriptor (EHD), which is one of the MPEG-7 texture descriptors , and wavelet moments . Recently, BDIP (block difference of inverse probabilities) and BVLC (block variation of local correlation coefficients) features have been proposed which effectively measure local brightness variations and local texture smoothness, respectively ]. These features are shown to yield better retrieval accuracy over the compared conventional features. They are extracted from 2X2 blocks into which a query image is partitioned to measure local image characteristics in great detail.

1.2 KEY CONCEPTS

Content-based image retrieval (CBIR) has become an active and fast-advancing research area in image retrieval . In a typical CBIR, features related to visual content such as shape, color, and texture are first extracted...
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