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Liu - Recent Advances in Intelligent Image Search and Video Retrieval

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Liu Recent Advances in Intelligent Image Search and Video Retrieval
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Springer International Publishing AG 2017
Chengjun Liu (ed.) Recent Advances in Intelligent Image Search and Video Retrieval Intelligent Systems Reference Library 10.1007/978-3-319-52081-0_1
1. Feature Representation and Extraction for Image Search and Video Retrieval
Qingfeng Liu 1
(1)
New Jersey Institute of Technology, Newark, NJ 07102, USA
(2)
California State University, Fullerton, CA 92834, USA
Qingfeng Liu (Corresponding author)
Email:
Yukhe Lavinia (Corresponding author)
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Abhishek Verma (Corresponding author)
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Joyoung Lee
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Lazar Spasovic
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Chengjun Liu (Corresponding author)
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Abstract
The ever-increasing popularity of intelligent image search and video retrieval warrants a comprehensive study of the major feature representation and extraction methods often applied in image search and video retrieval. Towards that end, this chapter reviews some representative feature representation and extraction approaches, such as the Spatial Pyramid Matching (SPM) , the soft assignment coding, the Fisher vector coding , the sparse coding and its variants, the Local Binary Pattern (LBP) , the Feature Local Binary Patterns (FLBP) , the Local Quaternary Patterns (LQP), the Feature Local Quaternary Patterns (FLQP) , the Scale-invariant feature transform (SIFT) , and the SIFT variants, which are broadly applied in intelligent image search and video retrieval .
1.1 Introduction
The effective methods in intelligent image search and video retrieval are often interdisciplinary in nature, as they cut across the areas of probability, statistics, real analysis, digital signal processing, digital image processing, digital video processing, computer vision, pattern recognition, machine learning, and artificial intelligence, just to name a few. The applications of intelligent image search and video retrieval cover a broad range from web-based image search (e.g., photo search in Facebook) to Internet video retrieval (e.g., looking for a specific video in YouTube). Figure displays some video frames from the cameras installed along the highways. Actually, the New Jersey Department of Transportation (NJDOT) operates more than 400 traffic video cameras, but current traffic monitoring is mainly carried out by human operators. Automated traffic incident detection and monitoring is much needed as operator-based monitoring is often stressful and costly.
Fig 11 Example images from the Caltech-256 dataset The ever-increasing - photo 1
Fig. 1.1
Example images from the Caltech-256 dataset
The ever-increasing popularity of intelligent image search and video retrieval thus warrants a comprehensive study of the major feature representation and extraction methods often applied in image search and video retrieval. Towards that end, this chapter reviews some representative feature representation and extraction approaches, such as the Spatial Pyramid Matching (SPM) [], and the SIFT variants, which are broadly applied in intelligent image search and video retrieval.
Fig 12 Example video frames from the cameras installed along the highways - photo 2
Fig. 1.2
Example video frames from the cameras installed along the highways
1.2 Spatial Pyramid Matching, Soft Assignment Coding, Fisher Vector Coding, and Sparse Coding
1.2.1 Spatial Pyramid Matching
The bag of visual words [] method, which enhances the discriminative capability of the conventional bag of visual words method by incorporating the spatial information.
Specifically, given the local feature descriptors Recent Advances in Intelligent Image Search and Video Retrieval - image 3 and the dictionary of visual words derived from the k-means algorithm the SPM method counts the frequency of the - photo 4 derived from the k-means algorithm, the SPM method counts the frequency of the local features over the visual words and represents the image as a histogram using the following hard assignment coding method:
11 In other words the SPM method activates only one non-zero coding - photo 5
(1.1)
In other words, the SPM method activates only one non-zero coding coefficient, which corresponds to the nearest visual word in the dictionary Picture 6 for each local feature descriptor Picture 7 . And given one image I with T local feature descriptors, the corresponding image representation is the probability density estimation of all the local features in this image I over all the visual words based on the histogram of visual - photo 8 in this image I over all the visual words based on the histogram of visual word frequencies as follows 12 - photo 9 based on the histogram of visual word frequencies as follows:
12 122 Soft Assignment Coding The histogram estimation of the density - photo 10
(1.2)
1.2.2 Soft Assignment Coding
The histogram estimation of the density function for the local features Picture 11 over the visual words Picture 12 , which violates the ambiguous nature of local features, is a very coarse estimation. Therefore, the soft assignment coding [], or kernel codebook , is proposed as a more robust alternative to histogram.
Specifically, the soft-assignment coding of is defined as follows 13 where is the smoothing parameter that controls - photo 13 is defined as follows:
13 where is the smoothing parameter that controls the degree of smoothness - photo 14
(1.3)
where Picture 15 is the smoothing parameter that controls the degree of smoothness of the assignment and Picture 16 is the exponential function.
Consequently, given one image I with T local feature descriptors, the corresponding image representation is the probability density estimation of the all the local features in this image I over all the visual words based on the kernel density - photo 17 in this image I over all the visual words based on the kernel density estimation using the Gaussian kernel as follows - photo 18
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