&lt;title&gt;Image indexing and retrieval techniques: past, present, and next&lt;/title&gt;

Shanbehzadeh, Jamshid; Moghadam, Amir Masoud Eftekhary; Mahmoudi, Fariborz

doi:10.1117/12.373578

Cited by 11 publications

(7 citation statements)

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“…The size and shape of segments that can be assigned by different resolutions is an active area of research (Tsai 2005). The perceptual features used in automatic low-level feature extraction include colour (Lai 2000;Wu et al 2000), texture (Howarth and Rüger 2004), shape content (Shanbehzadeh et al 2000) and spatial object layout recognition (Mandal et al 1999). Images may be annotated off-line manually, typically with information such as the image's creator, creation date or event activity (Eakins and Graham 1999).…”

Section: Related Workmentioning

confidence: 99%

Improving image annotation via useful representative feature selection

Lin

Oakes

Tait³

et al. 2008

Cogn Process

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This paper describes the automatic assignment of images into classes described by individual keywords provided with the Corel data set. Automatic image annotation technology aims to provide an efficient and effective searching environment for users to query their images more easily, but current image retrieval systems are still not very accurate when assigning images into a large number of keyword classes. Noisy features are the main problem, causing some keywords never to be assigned to their correct images. This paper focuses on improving image classification, first by selection of features to characterise each image, and then the selection of the most suitable feature vectors as training data. A Pixel Density filter (PDfilter) and Information Gain (IG) are proposed to perform these respective tasks. We filter out the noisy features so that groups of images can be represented by their most important values. The experiments use hue, saturation and value (HSV) colour feature space to categorise images according to one of 190 concrete keywords or subsets of these. The study shows that feature selection through the PDfilter and IG can improve the problem of spurious similarity.

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Section: Related Workmentioning

confidence: 99%

Improving image annotation via useful representative feature selection

Lin

Oakes

Tait³

et al. 2008

Cogn Process

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“…Extensive surveys of retrieval by image content include [10], [11]. Techniques for image retrieval are mainly based on the paradigm of query by example (QBE).…”

Section: A Image Retrievalmentioning

confidence: 99%

Extracting semantics from audio-visual content: the final frontier in multimedia retrieval

Naphade

Huang

2002

IEEE Trans. Neural Netw.

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Multimedia understanding is a fast emerging interdisciplinary research area. There is tremendous potential for effective use of multimedia content through intelligent analysis. Diverse application areas are increasingly relying on multimedia understanding systems. Advances in multimedia understanding are related directly to advances in signal processing, computer vision, pattern recognition, multimedia databases, and smart sensors. We review the state-of-the-art techniques in multimedia retrieval. In particular, we discuss how multimedia retrieval can be viewed as a pattern recognition problem. We discuss how reliance on powerful pattern recognition and machine learning techniques is increasing in the field of multimedia retrieval. We review the state-of-the-art multimedia understanding systems with particular emphasis on a system for semantic video indexing centered around multijects and multinets. We discuss how semantic retrieval is centered around concepts and context and the various mechanisms for modeling concepts and context.

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“…Of (1) IIvfI I(z7 + 2z8 + z9)-(z1 + 2z2 + Z3 ) +J(z3 +2z6 +z,)-(z1 +2z4 +z7jI +I(z6 +2z8 + z9)-(z, +2z2 +z41 +I(z4 +2z7 +Z8)-(z2 +2z3 +Z6) (2) Although Sobel's edge detector generally works well, it must repeatedly go over the whole image many times. As a result, this method requires W*H*32 time to operate on an image of size W*H. In this subsection, we use our proviously proposed method [8] to perform edge detection, called the prompt edge detection, which is not only simple but also efficient.…”

Section: Vf_=mentioning

confidence: 99%

“…In particular, content-based retrieval has become a hot research topic and, consequently, improving the technology for content-based querying systems is more challenging [1].…”

Section: Introductionmentioning

confidence: 99%

“…The content of image can be classified into two categories [1]: one is the characteristic of pixel value, such as color distribution, texture information of image, and the shape property of object in an image, another is the spatial relationship between objects. Current technologies of CBIR have combined different features to achieve the matching process [2], such as the combination of color and shape [3,4], the combination of color, texture, and shape [5,6] etc.…”

Section: Introductionmentioning

confidence: 99%

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On the Use of Edge Orientation and Distance for Content-Based Image Retrieval

Lin

Kao

Liang

et al.

2005 International Conference on Neural Networks and Brain

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Recently, various features for content-based image retrieval (CBIR) have been proposed, such as texture, color, shape, and spatial features. In this paper we propose a new feature, called orientation-distance histogram for CBIR. Firstly, we transform the RGB color model of a given image to the HSI color model and detect edge points by using the H-vector information. Secondly, we evaluate the orientation-distance histogram from the edge points to form a feature vector. After normalization of feature, our proposed method can cope with most problems of variations in image. Finally, we show some results of query for real life images with the precision and recall rates to measure the performance. The experimental results show that the proposed retrieval method is efficient and effective.

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<title>Image indexing and retrieval techniques: past, present, and next</title>

Cited by 11 publications

References 10 publications

Improving image annotation via useful representative feature selection

Improving image annotation via useful representative feature selection

Extracting semantics from audio-visual content: the final frontier in multimedia retrieval

On the Use of Edge Orientation and Distance for Content-Based Image Retrieval

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