JAVA实现的Locality Sensitive Hash

我在之前的博客已大致介绍了LSH的原理及其的适用场景，有兴趣的朋友可以移步至 
http://grunt1223.javaeye.com/blog/937600 

这里我给出它的具体实现及实验效果：

private int dimention; //维度大小，例如对于sift特征来说就是128 private int max; //所需向量中元素可能的上限，譬如对于RGB来说，就是255 private int hashCount; //哈希表的数量，用于更大程度地削减false positive //LSH随机选取的采样位数，该值越小，则近似查找能力越大，但相应的false positive也越大；若该值等于size，则为由近似查找退化为精确匹配 private int bitCount; private int size; //转化为01字符串之后的位数，等于max乘以dimensions private int[][] hashFamily; //LSH哈希族，保存了随机采样点的INDEX VectorComparator comparator; // private HashMap<String, ArrayList<IdentifiedVector>> map; private HashMap<String, ArrayList<String>> map; public SimpleLSH(int dimention, int max, int hashCount, int bitCount) { this.dimention = dimention; this.max = max; this.hashCount = hashCount; this.bitCount = bitCount; this.size = this.dimention * this.max; this.hashFamily = new int[hashCount][bitCount]; // map = new HashMap<String, ArrayList<IdentifiedVector>>(); map = new HashMap<String, ArrayList<String>>(); this.comparator = new VectorComparator(new int[] { 0 }); } //生成随机的投影点 private void generataHashFamily() { Random rd = new Random(); for (int i = 0; i < hashCount; i++) { for (int j = 0; j < bitCount; j++) { hashFamily[i][j] = rd.nextInt(size); } } //将向量转化为二进制字符串，比如元素的最大范围255，则元素65就被转化为65个1以及190个0 private int[] unAray(int[] data) { int unArayData[] = new int[size]; for (int i = 0; i < data.length; i++) { for (int j = 0; j < data[i]; j++) { unArayData[i * max + j] = 1; } } return unArayData; } //将向量映射为LSH中的key private String generateHashKey(int[] list, int hashNum) { StringBuilder sb = new StringBuilder(); int[] tempData = unAray(list); int[] hashedData = new int[bitCount]; //首先将向量转为二进制字符串 for (int i = 0; i < bitCount; i++) { hashedData[i] = tempData[hashFamily[hashNum][i]]; sb.append(hashedData[i]); // System.out.print(hashedData[i]); } //再用常规hash函数比如MD5对key进行压缩 MessageDigest messageDigest = null; try { messageDigest = MessageDigest.getInstance("MD5"); } catch (NoSuchAlgorithmException e) { } byte[] binary = sb.toString().getBytes(); byte[] hash = messageDigest.digest(binary); String hashV = MathUtils.getHexDigest(hash); return hashV; } //将向量映射为LSH中的key，并保存至map中 private void generateHashMap(String id, int[] vercotr) { for (int j = 0; j < hashCount; j++) { String key = generateHashKey(vercotr, j); ArrayList<String> l; if (map.containsKey(key)) { l = map.get(key); } else { l = new ArrayList<String>(); } l.add(id); map.put(key, l); } } // 查询与输入向量最接近（海明空间）的向量 public Set<String> query(int[] data) { // Set<IdentifiedVector> result = new HashSet<IdentifiedVector>(); Set<String> result = new HashSet<String>(); for (int j = 0; j < hashCount; j++) { String key = generateHashKey(data, j); if (map.containsKey(key)) { result.addAll(map.get(key)); } } return result; }  

我利用上面的LSH对图片的边缘直方图特征进行建模，获得了不错的效果，可以用于近似图片的查找，效果如下： 

 

 

转载：http://grunt1223.javaeye.com/blog/944894