Improving bag-of-visual-words image retrieval with predictive clustering trees

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The recent overwhelming increase in the amount of available visual information, especially digital images, has brought up a pressing need to develop efficient and accurate systems for image retrieval. State-of-the-art systems for image retrieval use the bag-of-visual-words representation of images. However, the computational bottleneck in all such systems is the construction of the visual codebook, i.e., obtaining the visual words. This is typically performed by clustering hundreds of thousands or millions of local descriptors, where the resulting clusters correspond to visual words. Each image is then represented by a histogram of the distribution of its local descriptors across the codebook. The major issue in retrieval systems is that by increasing the sizes of the image databases, the number of local descriptors to be clustered increases rapidly: Thus, using conventional clustering techniques is infeasible. Considering this, we propose to construct the visual codebook by using predictive clustering trees (PCTs), which can be constructed and executed efficiently and have good predictive performance. Moreover, to increase the stability of the model, we propose to use random forests of predictive clustering trees. We create a random forest of PCTs that represents both the codebook and the indexing structure. We evaluate the proposed improvement of the bag-of-visual-words approach on three reference datasets and two additional datasets of 100K images and 1M images, compare it to two state-of-the-art methods based on approximate k-means and extremely randomized tree ensembles. The results reveal that the proposed method produces a visual codebook with superior discriminative power and thus better retrieval performance while maintaining excellent computational efficiency.