A Relevance Feedback System for CBIR with Long-Term Learning

Lu, Hui; Huang, Xianglin; Yang, Lifang; Liu, Min

doi:10.1109/mines.2010.151

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Long term learning using relevance feedback approaches for image retrieval are discussed in [22][23][24][25][26][27]. The techniques based on long term learning uses an index table for maintaining historical data.…”

Section: Relevance Feedback System For Cbirmentioning

confidence: 99%

Intelligent Image Retrieval Techniques: A Survey

Yasmin¹,

Mohsin²,

Sharif³

2014

Journal of Applied Research and Technology

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In the current era of digital communication, the use of digital images has increased for expressing, sharing and interpreting information. While working with digital images, quite often it is necessary to search for a specific image for a particular situation based on the visual contents of the image. This task looks easy if you are dealing with tens of images but it gets more difficult when the number of images goes from tens to hundreds and thousands, and the same contentbased searching task becomes extremely complex when the number of images is in the millions. To deal with the situation, some intelligent way of content-based searching is required to fulfill the searching request with right visual contents in a reasonable amount of time. There are some really smart techniques proposed by researchers for efficient and robust content-based image retrieval. In this research, the aim is to highlight the efforts of researchers who conducted some brilliant work and to provide a proof of concept for intelligent content-based image retrieval techniques.

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Section: Relevance Feedback System For Cbirmentioning

confidence: 99%

Intelligent Image Retrieval Techniques: A Survey

Yasmin¹,

Mohsin²,

Sharif³

2014

Journal of Applied Research and Technology

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“…These image data must be well organized to realize rapid and efficient browsing and retrieval. Content-based Image Retrieval (CBIR) provides us a way to search images in large image database through the visual contents of themselves, such as color, texture, shape and spatial relationship [1,2] . Among these visual features, color features are the most expressive and simple features.…”

Section: Introductionmentioning

confidence: 99%

An Effective Similarity Measurement Algorithm for Dominant Color Feature Matching in Image Retrieval

Huang

et al. 2012

AMM

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For the reason that dominant colors can characterize color information of image region and can represent the image using fewer dimensions, it is one of the widely used color features in image retrieval. We extract the dominant color feature in HSV color space, and combine it with color distribution information. In this paper, a new similarity measurement algorithm based on block distance is proposed for dominant color matching. Our proposed algorithm not only takes the distance between dominant colors into account, but also the difference of the percentage of dominant colors. The average precision of our algorithm improves about 5% and about 14% respectively compared with block distance and Euclidean distance. Although the average precision of our algorithm is almost equal to quadratic form distance, the computation cost of our algorithm is obviously less than it.

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