63,330 research outputs found
Searching objects of interest in large scale data
Title from PDF of title page (University of Missouri--Columbia, viewed on October 31, 2012).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Dissertation advisor: Dr. Tony X. HanIncludes bibliographical references.Vita.Ph. D. University of Missouri--Columbia 2012."July 2012"The research on object detection/tracking and large scale visual search/recognition has recently gained substantial progress and has started to contribute to improving the quality of life worldwide: real-time face detectors have been integrated into point-and-shoot cameras, smart phones, and tablets; content-based image search is available at Google and Snaptell of Amazon;vision-based gesture recognition has been an indispensable component of the popular Kinect game console. In this dissertation, we investigate computer vision problems related to object detection, adaptation, tracking and content based image retrieval, all of which are indispensable components of a video surveillance system or a robot system. Our contribution involves feature development, exploration of detection correlations, object modeling, local context information of descriptors. More specifically, we designed a feature set for object detection with occlusion handling. To improve the detection performance on a video, we proposed a non-parametric detector adaptation algorithm to improve the performance of state of the art detectors for each specific video. To effectively track the detected object, we introduce a metric learning framework to unify the appearance modeling and visual matching. Taking advantage of image descriptor appearance context as well as local spatial context, we achieved state of the art retrieval performance based on the vocabulary tree based image retrieval framework. All the proposed algorithms are validated by throughout experiments.Includes bibliographical references
Hausdorff-Distance Enhanced Matching of Scale Invariant Feature Transform Descriptors in Context of Image Querying
Reliable and effective matching of visual descriptors is a key step for many vision applications, e.g. image retrieval. In this paper, we propose to integrate the Hausdorff distance matching together with our pairing algorithm, in order to obtain a robust while computationally efficient process of matching feature descriptors for image-to-image querying in standards datasets. For this purpose, Scale Invariant Feature Transform (SIFT) descriptors have been matched using our presented algorithm, followed by the computation of our related similarity measure. This approach has shown excellent performance in both retrieval accuracy and speed
A Sub-block Based Image Retrieval Using Modified Integrated Region Matching
This paper proposes a content based image retrieval (CBIR) system using the
local colour and texture features of selected image sub-blocks and global
colour and shape features of the image. The image sub-blocks are roughly
identified by segmenting the image into partitions of different configuration,
finding the edge density in each partition using edge thresholding followed by
morphological dilation. The colour and texture features of the identified
regions are computed from the histograms of the quantized HSV colour space and
Gray Level Co- occurrence Matrix (GLCM) respectively. The colour and texture
feature vectors is computed for each region. The shape features are computed
from the Edge Histogram Descriptor (EHD). A modified Integrated Region Matching
(IRM) algorithm is used for finding the minimum distance between the sub-blocks
of the query and target image. Experimental results show that the proposed
method provides better retrieving result than retrieval using some of the
existing methods.Comment: 7 page
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A words-of-interest model of sketch representation for image retrieval
In this paper we propose a method for sketch-based image retrieval. Sketch is a magical medium which is capable of conveying semantic messages for user. It’s in accordance with user’s cognitive psychology to retrieve images with sketch. In order to narrow down the semantic gap between the user and the images in database, we preprocess all the images into sketches by the coherent line drawing algorithm. During the process of sketches extraction, saliency maps are used to filter out the redundant background information, while preserve the important semantic information. We use a variant of Words-of-Interest model to retrieve relevant images for the user according to the query. Words-of-Interest (WoI) model is based on Bag-ofvisual Words (BoW) model, which has been proven successfully for information retrieval. Bag-of-Words ignores the spatial relationships among visual words, which are important for sketch representation. Our method takes advantage of the spatial information of the query to select words of interest. Experimental results demonstrate that our sketch-based retrieval method achieves a good tradeoff between retrieval accuracy and semantic representation of users’ query
Visual Information Retrieval in Endoscopic Video Archives
In endoscopic procedures, surgeons work with live video streams from the
inside of their subjects. A main source for documentation of procedures are
still frames from the video, identified and taken during the surgery. However,
with growing demands and technical means, the streams are saved to storage
servers and the surgeons need to retrieve parts of the videos on demand. In
this submission we present a demo application allowing for video retrieval
based on visual features and late fusion, which allows surgeons to re-find
shots taken during the procedure.Comment: Paper accepted at the IEEE/ACM 13th International Workshop on
Content-Based Multimedia Indexing (CBMI) in Prague (Czech Republic) between
10 and 12 June 201
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