[Reading] Image Retrieval: Ideas, Influences, and Trends of the New Age

I choose to summarize from 3. IMAGE RETRIEVAL TECHNIQUES: ADDRESSING THE CORE PROBLEM (p.14) to 3.2. Image Similarity Using Visual Signature (p.30).

CBIR technology amounts to 2 problems: (a) the design of image description (signature), and (b) the similarity measure between two image descriptions. In the recent years, the design of features and the signatures constructed by these features have much progress. Besides, using machine learning techniques in CBIR has become more popular and also important.

Signature Extraction

Feature extraction is the first step, after we extract features from an image, we need to do signature construction using these features. There are 2 ways to do signature construction: (a) using segmentation as first step, and (b) segmentation-free approach.

To acquire a region-based signature, image segmentation is needed. Several methods proposed to do segmentation on medical images. Segmentation-based approach may have the problem that result is too sensitive to segmentation quality, so several methods tried to solve these problems.

Computing global feature is efficient, but it is insensitive to location. So a better way (also a trend) is to compute local features then summarize them. Several types of local features are discussed such as color, texture, shape, spatial modeling and interesting points.

When the # of features are very large for us to choose, we can use machine learning techniques to do feature selection.

For constructing region-based signature, several methods proposed to do signature construction. Lots of them have a connection with histograms.

Similarity

There are 3 types of signatures: (a) region-based signature, (b) feature vector, and (c) summary of local feature vectors. Different types of signatures have different similarity measures. For (a), the definition of distance between “set of vectors” is crucial. For (b), several recent efforts have been made to measure the distance on a manifold, because using geodesic as distance measure is more reasonable. For (c), codebooks and probability density functions have been used as signatures.

For region-based signature, there are basically 2 formulations to compute similarity, one is using the sum of weighted pair-wise distance as formulation, different constraints lead to different design of weights. Another approach is using Hausdorff distance. Recently, several improvements have been made including feature tuning, weight computation, robustness against inaccurate segmentation and speeding-up retrieval.

For feature vector, computation of similarity is performed nonlinearly along the manifold, typical methods are locally-linear embedding (LLE), isomapping, and multidimensional scaling.

[Reading] How to give a good research talk

This artical gives suggestions about giving a presentation of 30-60 minutes. Because it says that "make what is useful for you, and ignore the rest", I only summarize the parts useful to me.
First, use examples is important. Always remember to illustrate an idea (theorem, definiiton, ...) WITH an example.
Second, treat the more important aspects in more detail than others. Also, don't read your slides, talk ABOUT what's on it.
Last, avoid too much introduction such as previous work. Also, sometimes give outline of your talk is not appropriate.

[Reading] How to Read a Paper

This paper propose a 3-pass method for reading papers.
(1) The 1st pass (5~10min) gives you a general idea by answering the 5 Cs to yourself.
(2) The 2nd pass (1hr) lets you grasp the content but not detail. At this stage you should be able to do summarization!
(3) The 3rd pass (4~5hr) helps you understand the paper in depth. The key is to attempt to "virtually re-implement it".

This paper also describe how to use the proposed method to do a survey by 3 steps.
(1) Use search engine and read "RELATED WORK".
(2) Find key citations and key researchers's recent publication.
(3) Quickly scan the top conferences' recent papers.