1. Presented by Xinyu Chang
Cross-Indexing of Binary Scale Invariant Feature Transform Codes for Large-Scale Image Search
Presented by Xinyu Chang

2. Introduction
Image matching is a fundamental aspect of many problems in computer vision, including object or scene recognition, solving for 3D structure from multiple images, stereo correspondence, and motion tracking.
In recent years, there has been growing interest in mapping visual features into compact binary codes for applications on large-scale image collections. Encoding high-dimensional data as compact binary codes reduces the memory cost for storage.

3. Introduction Goal Extracting distinctive invariant features
Correctly matched against a large database of features from many images
Invariance to image scale and rotation
Robustness to
Affine distortion
Change in 3D viewpoint
Addition of noise
Change in illumination

4. Introduction

5. Content Interest Point Detection Flexible Binarization Cross Indexing
Scale-space extrema detection
Keypoint localization
Orientation assignment
Keypoint descriptor
Flexible Binarization
Cross Indexing
Result

6. Interest Point Detection

7. Interest Point Detection

8. Interest Point Detection

9. Interest Point Detection

10. Initial Outlier Rejection
Dog is most stable across scale

11. Interest Point Detection

12. Rotation invariance To achieve rotation invariance
Compute central derivatives, gradient magnitude and direction of L (smooth image) at the scale of key point (x,y)

13. Rotation invariance

14. Rotation invariance

15. Rotation invariance

16. Key point descriptor

17. FLEXIBLE SIFT BINARIZATION
Given an image, the detected interest points are denoted by { fi }n−1 i=0 , in which N represents the total number of the detected interest points. Each feature fi includes a L2-normalized descriptor di ∈ RD, for SIFT descriptor D is 128. Our target is to transform local feature descriptor di to an L-bit binary code string B = {b0, b1, , bL−1}

18. FLEXIBLE SIFT BINARIZATIOND
where C represents the 3-D comparison array with size
D × D × 2. And C(i, j ) means the comparison result
between the magnitudes in the i -th and the j -th dimension
of descriptor d. α is a scalar threshold whose impact will
be studied in the experiment section.

19. FLEXIBLE SIFT BINARIZATION
And concatenate them into a comparison string S with
β = 2D(D − 1) bits in total, as shown by the second step in
Fig. 2. To simplify the notations, in the following, S is denoted
as S = {s0, s1, s2, , sβ−1}. To obtain an L-bit binary code
B = {b0, b1, , bL−1}, next we encode the comparison string
S into L bits.

20. FLEXIBLE SIFT BINARIZATION

21. CROSS-INDEXING STRATEGY
Code Word
the first 32 bits of the
binary code is code word.
The visual words are generated by
clustering the randomly selected SIFT descriptor. Each feature
is assigned to a visual word by nearest neighbor approach or
approximate nearest neighbor approach.

22. CROSS-INDEXING STRATEGY
In the BoVW model, an image is represented by a visual
word histogram with tf -idf weighting strategy. The
similarity between two images are measured by the L1 or L2
distance of their visual word vectors. In the binary code based
retrieval system, the features’ binary codes are used to find the
true matches and we use the number of matches to measure
the similarity between two images, denoted by Scorei. And
this strategy can be formulated by
in which i represents the i -th database image. B(d) and B(q)
denote the binary SIFT code of the database feature d and
the query feature q, respectively. T is a pre-defined threshold
value. The impact of T will be studied in our experimental
part. H(・, ・) denotes the Hamming distance between two
binary SIFT codes. If two images have the same score value,
we favor the image with fewer features.

23. CROSS-INDEXING STRATEGY

24. CROSS-INDEXING STRATEGY

25. Result

26. Result

27. Thank you