Objective

2

Hashing method is one of the most popular approaches for content-based image retrieval. The main idea of this approach is to learn the same size of binary codes for each image and then use the Hamming distance to measure the similarity of images. Effective Hashing methods should include at least three properties. First

the learned codes should be short so that large amounts of images can be stored in a small memory. Second

the learned codes should transform images that are perceptually or semantically similar into binary strings with a small Hamming distance. Third

the method should be efficient to learn the parameters of the binary code and encode a new test image. Most Hashing approaches include two important steps to achieve binary coding

namely

projection and quantization. For projection

most Hashing approaches perform principal component analysis (PCA) to reduce the dimensionality of raw data. For quantization

different Hashing approaches may design different strategies. In the quantization stage

most traditional Hashing methods usually allocate the same number of bit to each data subspace for image retrieval. However

information quantities are different in each data subspace. Accordingly

a uniform quantization may result in inefficient codes and high quantization distortion problems

especially when the data have unbalanced information quantities. To address this problem

this study proposes an effective coding method based on product quantization

called Huffman coding.

Method

2

Similar to most Hashing approaches

the proposed method utilizes PCA to reduce the dimensionality of raw data in the projection stage. A vector quantization scheme is then carefully designed at the quantization stage. The proposed approach first utilizes product quantization to quantize data after dimensionality reduction to preserve data distribution in the original space. For each subspace

the variance can be directly calculated as the measure of its information quantity. For effectiveness

the subspace with high information quantity should be allocated with a large number of bit for binary coding and vice versa. To achieve this goal

the reciprocal value of the variance proportion can be used to build a Huffman tree

which can then be applied to generate Huffman codes. Accordingly

different bit and values of binary code can be assigned to each subspace. In other words

numerous bit will be allocated to encode subspaces with large variance and few for subspaces with small variance. The variance is easy to calculated

and therefore

the proposed approach is simple and efficient for binary coding. Experimental results illustrate that the Huffman coding method is effective for image retrieval.

Result

2

During the experiment

the proposed approach is tested on three public datasets

namely

MNIST

NUS-WIDE

and 22K LabelMe. For each image

a 512D GIST descriptor can be extracted as the input of the Hashing approach. To verify its good performance

the proposed approach is compared with four related approaches:original product quantization method

PCA-based product quantization method

iterative quantization method

and transform coding (TC) method. The experimental results are reported in the form of quantization distortion

mean average precision

recall

and training time. Results show that the average quantization distortion of the proposed approach can be decreased by approximately 49%

and the mean average precision of the retrieval results is increased by approximately 19% compared with the existing method based on product quantization. The training time of the proposed approach is also compared with that of TC from 32 bit to 256 bit on MNIST. The proposed approach can reduce 22.5 s of the training time on average.

Conclusion

2

This study proposes Huffman coding for image retrieval in the product quantization stage. According to information quantities

the Huffman-based product quantization scheme can allocate different numbers of bit to each data subspace

which can effectively increase coding efficiency and quantization accuracy. The proposed approach is tested on three public datasets and compared with four related approaches. Experimental results demonstrate that the proposed approach is superior to some state-of-the-art algorithms for image retrieval on mean average precision and recall. The proposed approach does not belong to precise coding methods; thus

our future work will focus on precise Hashing method for effective image retrieval.