Viirya

Jonhnny Weslley gave an interesting post about how to use Scala with Hadoop.

However, due to Hadoop API changes and other reasons, the codes from Weslley can not work as I tried for hadoop-0.20.0.

I have tried a version that works with hadoop-0.20.0. The most tricky part is the generics of Java in Scala.

package shadoop

import SHadoop._
import java.util.Iterator
import org.apache.hadoop.fs._
import org.apache.hadoop.io._
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.mapred.OutputCollector
import org.apache.hadoop.mapred.Reporter;

import org.apache.hadoop.mapreduce.Job
import org.apache.hadoop.mapreduce.Mapper
import org.apache.hadoop.mapreduce.MapContext
 

In order to support one of our ongoing projects, I developed a PHP extension for PageRank algorithm to conduct search result re-ranking from a PHP frontend. Using pure PHP to do the algorithm is consuming too much time for real-time response when the scale of re-ranking becomes larger.

This PHP extension uses cblas and atblas libraries to speed up matrix computing. It also applies OpenMP on few places.

The PHP extension on github.

After installing the .so file of this extension, you have a pagerank function in PHP to use.

Usage example:

$array = array(
array('0', '1', 0.5),
array('0', '2', 0.5),
array('2', '0', 0.33333),

Based on Jeff Sharkey's android-sky codes, there is a Chinese weather widget on android.

I have changed the weather information source from National Weather Service NDFD API to Google Weather API for global weather information (I suppose it is).

The configuration interface is not changed at all. Installing it will replace Jeff Sharkey's original package installed already.

You can download the APK file here.

"Contour and Texture Analysis for Image Segmentation"

By JITENDRA MALIK, SERGE BELONGIE, THOMAS LEUNG AND JIANBO SHI

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This paper focuses on the image segmentation problem. It tries to partition grayscale images into disjoint regions of coherent brightness and texture. The novelty of the proposed algorithm falls considering both texture and contour for image segmentation.

They apply the approach of textons, the prototypes of linear filter outputs, to map each pixel to the texton nearest to its vector of filter response.

The adopt the approach to convolve the image with a bank of linear spatial filters. The oriented filterbank used in this work is based on rotated copies of a Gaussian derivative and its Hilbert transform.

"Shape matching and object recognition using shape contexts"

By Serge Belongie, Jitendra Malik and Jan Puzicha

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The authors in this work try to operationalize the notion of shape similarity.

The way they approach this problem is to find correspondences between shapes.

First they represent shapes as a set of points sampled from shape contours. They introduce a shape descriptor, shape context, to describe a given point on a shape by the coarse distribution of the rest of the shape.

Finding correspondences between two shapes is equivalent to finding the point that has the most similar shape context on one shape with respect to each point on the other shape.

"Nonlinear dimensionality reduction by locally linear embedding"

By Sam T. Roweis and Lawrence K. Saul

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The high-dimensional data cause problems in data analysis and visualization in many areas of science. We need an efficient and effective approach to reduce dimensionality for a compact representation of high-dimensional data.

In this paper, locally linear embedding (LLE) has been proposed as an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs.

"Eigenfaces for recognition"

By Matthew Turk, Alex Pentland

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Computational models for face recognition contribute not only to theoretical insights but also practical application. However, it is a difficult problem.

The eigenface approach proposed by Matthew Turk and Alex Pentland in this paper contributes a simple yet mathematically well-formed model based on solving eigenvectors of covariance matrix obtained from training face images.

This work turns its attention to the information content of face images, emphasizing the significant local and global features that may not be directly related to our intuitive notion of face features such as the eyes, nose, lip, and hair.

"Scale & Affine Invariant Interest Point Detectors"

By KRYSTIAN MIKOLAJCZYK AND CORDELIA SCHMID

--

In this paper, the authors propose a novel approach to detect interest points invariant to scale and affine transformations.

Their scale and affine invariant detectors are based on three results:

1. Interest points detected by Harris detector can be adapted to affine transformation.
2. The characteristic scale of a local structure is indicated by a local extrema over.
3. The affine shape of a point neighborhood is estimated based on second moment matrix.

"Distinctive Image Features from Scale-Invariant Keypoints"

By David G. Lowe

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This paper proposes the Scale Invariant Feature Transform (SIFT) approach for image matching problem. It is widely used in computer vision and image retrieval related fields.

It is a method to extract local interest points from images. Importantly, these feature points are invariant to image scale, rotation. It includes four main stages:

1. Scale-space extrema detection
2. Keypoint localization
3. Orientation assignment
4. Keypoint descriptor generation

*Scale-space extrema detection

"Image Retrieval: Ideas, Influences, and Trends of the New Age"

By RITENDRA DATTA, DHIRAJ JOSHI, JIA LI, and JAMES Z. WANG, 2008

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This is a comprehensive paper in which the authors review the theoretical and empirical contributions related to image retrieval.

The article consists of several sections. First it discusses critical aspects of the design of real-world image retrieval systems. For these aspects, some key approaches and techniques of the current decade are presented. As the research of CBIR evolves in the decade, new problems and applications related to image retrieval are brought out.

Two intrinsic problems are derived from the nature of CBIR task:

1. How to represent an image mathematically
2. How to calculate the similarity between images