Keun-Dong Lee

An analysis of the effect of different image preprocessing techniques on the performance of SURF: Speeded Up Robust Features

In this paper, we analyze the effect of different image preprocessing techniques on the performance of Speeded Up Robust Features, SURF. We investigate the effects of the techniques like Histogram Equalization, Multiscale Retinex, and Image Adaptive Contrast Enhancement (IACE) scheme that we propose, on the SURF in terms of its feature points detection, and computational time for extracting the descriptors. We then test the effect of these image preprocessing techniques on the repeatability of the state-of-the-art detectors like Harris-Affine, Hessian-Affine, MSER, Edge Based Regions, Intensity Based Regions, and SURF. We carry out the repeatability test on the standard images which have been used as a benchmark for the evaluation of the performance of other schemes for the detection of feature points. Finally, we propose a method for scaling large resolution images that can be used in conjunction with the IACE method to enhance the matching speed of SURF, along with maintaining the accuracy and the standard of its performance.

Face recognition using LBP for personal image management system and its analysis

In this paper, LBP and its variant were tested on Family Face Database which was constructed in the earlier work by the author [1] in the framework of personal image management system. The combination of LBP and other features were also tested for comparison. In addition, several illumination normalizing methods, dissimilarity measures and sub-block divisions were also tested in our framework.

Extensive analysis of feature selection for compact descriptor

In this paper, feature selection criteria of local descriptors are examined with well-defined evaluation framework in MPEG-7 compact descriptor for visual search (CDVS) [6]. The effect of feature selection on descriptor was analyzed in compressed and uncompressed domain. Various combinations of feature characteristics such as scale and orientation of features, distance from center, and response of difference of Gaussian (DoG) [5] were examined for feature selection criterion via pair-wise matching experiments in MPEG-7 CDVS datasets.

Accelerating Local Feature Extraction Using Two Stage Feature Selection and Partial Gradient Computation

In this paper, we present a fast local feature extraction method, which is our contribution to ongoing MPEG standardization of compact descriptor for visual search (CDVS). To reduce time complexity of feature extraction, two-stage feature selection, which is based on the feature selection method of CDVS Test Model (TM), and partial gradient computation are introduced. The proposed method is examined on SIFT and compared to SIFT and SURF extractor with the previous feature selection method. In addition, the proposed method is compared to various feature extraction methods of the current CDVS TM 11 in CDVS evaluation framework. Experimental results show that the proposed method significantly reduces the time complexity while maintaining the matching and retrieval performance of previous work. For its efficiency, the proposed method has been integrated into CDVS TM since \(107^{\text {th}}\) MPEG meeting. This method will be also useful for feature extraction on mobile devices, where the use of computational resource is limited.

Design and implementation of interactive visual search system for Smart TV

This paper presents a system that allows users to search for information about video content on a Smart TV. The proposed system has two major components: a Smart TV and a mobile device. The motion sensing interface in the Smart TV interprets gestures and requests the relevant information from the server. The mobile device displays search results using information received from the server. With the recent development of mobile visual search technology and motion sensing devices, experimental results confirm that the proposed system provides an efficient way to search for information via Smart TVs.

Intensity comparison based compact descriptor for mobile visual search

In this paper, we proposed intensity comparison based compact descriptor for mobile visual search. For practical mobile applications, the low complexity and the descriptor size are more preferable, and many algorithms such as SURF, CHoG, and PCA-SIFT have been proposed. However, these approaches focused on not the feature description but the extraction time and the size of the feature. This paper suggests feature description method based on simple intensity comparison with considering descriptor size and extraction speed. Experimental results show that the proposed method has comparable performance to SURF with similar complexity and 20 times much smaller size.

Street searching service framework for navigation

In this paper, we propose new street searching service framework. Most location-based service uses GPS to estimate the geographical position; however, it is noisy and the GPS is not always available. To compensate this error, the visual search can be used to localize the position by comparing descriptors with geo-tagged reference DB, but it is a tedious job to collect the reference DB. To overcome this problem, we use visual descriptors around crossroads to localize the position. Combining location recognition with visual descriptor and multi-perspective panoramic street views, we provide precise and easy way of searching locations in downtown.