Abstract
In recent years, taking pictures with digital camera becomes more and more popular. Unlike traditional photos, the cost of taking a digital photo is nearly free, so users often have a great number of digital pictures. For the purpose of management, those pictures should be well categorized. However, grouping a lot of pictures by hand is a difficult and boring task. As a result, how to use computer to automatically group numerous and chaotic digit pictures efficiently is an important research challenge. In this thesis, we propose a content-based picture clustering method for this topic. The proposed method can be separated into three phases. First, phase I extracts local SIFT (Scale Invariant Feature Transformation) features and global MPEG-7 CLD (Color Layout Descriptor) features from all input pictures. SIFT features can describe distinctive local characteristics of an image excluding the color information. Then, we add the color feature to compensate the problem of SIFT. In phase II, we adopt the Affinity Propagation (AP) algorithm as our image clustering method. Further, we improve the instability by appending an estimating step that can evaluate a more suitable initial setting of AP. Finally, phase III is the post-processing stage that merges those small and similar groups produced in phase II. The experimental results show that the proposed method has over 80% ARI accuracy score for 1000 pictures. When the size of the dataset expands to 3000 pictures, the ARI accuracy score is still 70%. On average, our proposed method has 54% improvement in terms of ARI score, as compared to the pure AP algorithm. All the experimental results show that the proposed clustering algorithm is effective and robust.