Английская грамматика
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Bag of ‘words’ Concept
Bag of ‘words’ Concept
Bag of ‘words’ Concept
Bag of ‘words’ Concept
Bag of ‘words’ Concept
Bag of ‘words’ Concept
Bag-of-Words Concept: Analogy to documents
Bag-of-Words Concept: Analogy to documents
Bag-of-Words Concept: Analogy to documents
Bag-of-Words Concept: Analogy to documents
Bag-of-Words Concept: Analogy to documents
Bag-of-Words Concept: Analogy to documents
Bag-of-Words Concept: Analogy to documents
Bag-of-Words Concept: Analogy to documents
Bag-of-Words Concept: Analogy to documents
Bag-of-Words Concept: Analogy to documents
Bag of ‘words’ Concept
Bag of ‘words’ Concept
2.
2.
2.
2.
Image Representation
Image Representation
Results : MAP of category based queries
Results : MAP of category based queries
Results : MAP of varied dictionary sizes
Results : MAP of varied dictionary sizes
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Content-Based Image Retrieval using the Bag-of-Words Concept

содержание презентации «Content-Based Image Retrieval using the Bag-of-Words Concept.ppt»
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1Content-Based Image Retrieval using 10Extraction. Represent each patch/interest
the Bag-of-Words Concept. Fatih Cakir point with SIFT descriptors [1 Lowe ‘99].
Melihcan Turk F. Sukru Torun Ahmet Cagri 10/23.
Simsek. 11Dictionary Formation : Vector
2Outline. Introduction Bag-of-Words Quantization. Vector quantization. 11/23.
Concept Dictionary Formation Content-Based 12Example Dictionary. 12/23.
Image Retrieval using BoW Results 13Example ‘Visual words’. 13/23.
Conclusion References. 2/23. 14Image Representation. codewords.
3Introduction : Motivation. CBIR frequency. 14/23.
motivation: Huge amount of multimedia 15Content Based Image Retrieval using
content demands a sophisticated analysis BoW. We saw have to represent images using
rather than simple textual processing the BoW concept. With histograms. It is a
(metadata such as annotations or mapping of classical text representation
keywords). Traditional methods for onto the image domain. Hence based on the
retrieving images is not very satisfactory similarity of histograms we can return
or may not meet user demand E.g. In Google ranked results, given an query image.
image typing ‘Apple’ returns the Apple Category search: Retrieving an arbitrary
products as well as the apple fruit. Main image representative of a specific class.
reason is the ambiguity in the language. Used a subset of Caltech 101 dataset [2].
Several other limitations. 3/23. 15/23.
4Introduction : Motivation. CBIR 16Content Based Image Retrieval using
systems compensates such issues by BoW. Given an query image return the top k
analyzing the actual ‘content’ of the most similar results. A ‘positive’ or
image hence yielding a more effective ‘true’ match considered to be within the
feature for describing the image rather same category. Mean average precision
than user defined meta-data Content may be value (MAP) is computed for each category
texture, color or any other information using 10 query images. 16/23.
that can be derived from the image itself. 17Content Based Image Retrieval using
One promising idea is to represents images BoW: Details. For vector quantization
as ‘words’ analogous to text retrieval K-means is used with K=3000. Hence the
solutions. Document ~ Image, term (word) ~ dictionary contains 3000 visual words and
visual word First introduces in [3]. 4/23. the histogram has 3000 bins representing
5Bag of ‘words’ Concept. 5/23. each visual word. L2-norm – Euclidean
6Bag-of-Words Concept: Analogy to distance is used for similarity measure.
documents. 6/23. Of all the sensory Visual words are represented using Lowe’s
impressions proceeding to the brain, the SIFT descriptors. Interest points are
visual experiences are the dominant ones. extracted using DOG (Difference of
Our perception of the world around us is Gaussians). For each of the 18 category 10
based essentially on the messages that query images are used and the average MAP
reach the brain from our eyes. For a long value is considered as the categories
time it was thought that the retinal image success rate. 17/23.
was transmitted point by point to visual 18Results : MAP of category based
centers in the brain; the cerebral cortex queries. 18/23.
was a movie screen, so to speak, upon 19Results : MAP of varied dictionary
which the image in the eye was projected. sizes. 19/23.
Through the discoveries of Hubel and 20Results. The ‘Motorbikes’ category has
Wiesel we now know that behind the origin the highest MAP rate (0.70). The lowest is
of the visual perception in the brain category ‘camera’ (0.07). Average of MAP
there is a considerably more complicated rates : 0.25 As the dictionary size get
course of events. By following the visual larger (i.e. more visual words) images are
impulses along their path to the various represented accurately, hence MAP values
cell layers of the optical cortex, Hubel increase Performance seem to converge
and Wiesel have been able to demonstrate after K>3000. 20/23.
that the message about the image falling 21Conclusion. Content-Based Image
on the retina undergoes a of nerve cells Retrieval systems has gained severe
stored in columns. In this system each interest among research scientists since
cell has its specific function and is multimedia files such as images and videos
responsible for a specific detail in the has dramatically entered our lives
pattern of the retinal image. throughout the last decade Textual
7Bag-of-Words Concept: Analogy to analysis is not sufficient for effective
documents. Each image can be represented retrieval systems Analogous to document
as a histogram . where each bin of the representation an image can be described
histogram corresponds to a visual word in by ‘visual words’. BoW concept. Using only
the dictionary and the value of the bin is such feature results are highly
the frequency of occurrence of such visual satisfying. 21/23.
word. 7/23. 22References. [1] D. G. Lowe.
8Bag of ‘words’ Concept. Hence, we Distinctive image features from
consider an image as a document. And as scale-invariant keypoints. IJCV,
words/terms define a document, visual 60(2):91–110, 2004
words define an image. Words are known? [2]http://www.vision.caltech.edu/Image_Dat
What are ‘visual words’? Need to define a sets/Caltech101/ [3] J. Sivic and A.
dictionary. 8/23. Zisserman. Video Google: A text retrieval
92. 1. Bag of ‘words’ Concept : approach to object matching in videos. In
Construct a dictionary. 9/23. Proc. ICCV, 2003. 22/23.
10Dictionary Formation: Feature 23Thank You! Questions and Demo! 23/23.
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