Evaluation on color spaces and distance measures for color histogram-based image retrieval

Content-Based Image Retrieval (CBIR) systems retrieve images from the image repository or database in which they are visually similar to the query image. CBIR plays an important role in various fields such as medical diagnosis, crime prevention, web-based searching, and architecture. CBIR consists mainly of two stages: The first is the extraction of features and the second is the matching of similarities. There are several ways to improve the efficiency and performance of CBIR, such as segmentation, relevance feedback, expansion of queries, and fusion-based methods. The literature has suggested several methods for combining and fusing various image descriptors. In general, fusion strategies are typically divided into two groups, namely early and late fusion strategies. Early fusion is the combination of image features from more than one descriptor into a single vector before the similarity computation, while late fusion refers either to the combination of outputs produced by various retrieval systems or to the combination of different rankings of similarity. In this study, a group of color and texture features is proposed to be used for both methods of fusion strategies. Firstly, an early combination of eighteen color features and twelve texture features are combined into a single vector representation and secondly, the late fusion of three of the most common distance measures are used in the late fusion stage. Our experimental results on two common image datasets show that our proposed method has good performance retrieval results compared to the traditional way of using single features descriptor and also has an acceptable retrieval performance compared to some of the state-of-the-art methods. The overall accuracy of our proposed method is 60.6% and 39.07% for Corel-1K and GHIM-10K ‎datasets, respectively.

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Color quantization and its impact on color histogram based image retrieval accuracy

2009 First International Conference on Networked Digital Technologies ◽

10.1109/ndt.2009.5272135 ◽

2009 ◽

Cited By ~ 14

Author(s):

Khouloud Meskaldji ◽

Samia Boucherkha ◽

Salim Chikhi

Keyword(s):

Image Retrieval ◽

Color Histogram ◽

Color Quantization ◽

Retrieval Accuracy

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Similar Image Retrieval Using Color Histogram in HSV Space and SIFT Descriptor with FLANN

Advances in Intelligent Systems and Computing - Foundations of Intelligent Systems ◽

10.1007/978-3-642-54924-3_103 ◽

2014 ◽

pp. 1085-1093

Author(s):

Yaru Wang ◽

HongXin Li ◽

LongKui Wang

Keyword(s):

Image Retrieval ◽

Color Histogram ◽

Similar Image ◽

Sift Descriptor

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Color Features and Color Spaces Applications to the Automatic Image Annotation

Biometrics ◽

10.4018/978-1-5225-0983-7.ch042 ◽

2017 ◽

pp. 1061-1083

Author(s):

Vafa Maihami ◽

Farzin Yaghmaee

Keyword(s):

Image Processing ◽

Digital Image ◽

Image Annotation ◽

Color Histogram ◽

Experimental Results ◽

Automatic Image Annotation ◽

Color Spaces ◽

Color Features ◽

Different Color ◽

Color Moment

Nowadays images play a crucial role in different fields such as medicine, advertisement, education and entertainment. Describing images content and retrieving them are important fields in image processing. Automatic image annotation is a process which produces words from a digital image based on the content of this the image by using a computer. In this chapter, after an introduction to neighbor voting algorithm for image annotation, we discuss the applicability of color features and color spaces in automatic image annotation. We discuss the applicability of three color features (color histogram, color moment and color Autocorrelogram) and three color spaces (RGB, HSI and YCbCr) in image annotation. Experimental results, using Corel5k benchmark annotated images dataset, demonstrate that using different color spaces and color features helps to select the best color features and spaces in image annotation area.

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