Since the development of food diaries could enable people to develop healthy eating habits, food image recognition is in high demand to reduce the effort in food recording. Previous studies have worked on this challenging domain with datasets having fixed numbers of samples and classes. However, in the real-world setting, it is impossible to include all of the foods in the database because the number of classes of foods is large and increases continually. In addition to that, inter-class similarity and intra-class diversity also bring difficulties to the recognition. In this paper, we attempted to solve these problems by using deep convolutional neural network features to build a personalized classifier which incrementally learns the user's data and adapts to the user's eating habit. As a result, we achieved the state-of-the-art accuracy of food image recognition by the personalization of 300 food records per user.

Currently, food image recognition tasks are evaluated against fixed datasets. However, in real-world conditions, there are cases in which the number of samples in each class continues to increase and samples from novel classes appear. In particular, dynamic datasets in which each individual user creates samples and continues the updating process often has content that varies considerably between different users, and the number of samples per person is very limited. A single classifier common to all users cannot handle such dynamic data. Bridging the gap between the laboratory environment and the real world has not yet been accomplished on a large scale. Personalizing a classifier incrementally for each user is a promising way to do this. In this paper, we address the personalization problem, which involves adapting to the user's domain incrementally using a very limited number of samples. We propose a simple yet effective personalization framework, which is a combination of the nearest class mean classifier and the 1-nearest neighbor classifier based on deep features. To conduct realistic experiments, we made use of a new dataset of daily food images collected by a food-logging application. Experimental results show that our proposed method significantly outperforms existing methods.

In general, object-detection methods apply classifiers to pre-calculated object proposals. It is therefore important to minimize the number of proposals to achieve computational efficiency. In this paper, we show that the region size for food objects in recorded images of daily food follows a lognormal distribution, which is different from the distribution for widely used datasets collected by querying the names of dishes. We explain this characteristic using Gibrat's law, and construct a model for the region-size distribution of objects in images. We applied the model to the filtering of object proposals generated by selective search and edge boxes. We obtained a significant reduction of 40.6% in the number of hypotheses compared with a conventional selective search, despite a decrease of only 0.007 in the Mean Average Best Overlap.

In this paper, we apply a convolutional neural network (CNN) to the tasks of detecting and recognizing food images. Because of the wide diversity of types of food, image recognition of food items is generally very difficult. However, deep learning has been shown recently to be a very powerful image recognition technique, and CNN is a state-of-the-art approach to deep learning. We applied CNN to the tasks of food detection and recognition through parameter optimization. We constructed a dataset of the most frequent food items in a publicly available food-logging system, and used it to evaluate recognition performance. CNN showed significantly higher accuracy than did traditional support-vector-machine-based methods with handcrafted features. In addition, we found that the convolution kernels show that color dominates the feature extraction process. For food image detection, CNN also showed significantly higher accuracy than a conventional method did.

A new line equalizer system for time compression burstmode digital suhscriber loop transmission that can equalize a line with multiple bridged taps (BT's) is developed. Using the developed equalizer, up to 5 km (0.4 mmφ) line length and four BT connections can be equalized at a data rate of 80 kbits/s in each direction. The equalizer is composed of a variable gain\surd{f}step equilizer, a decision feedback automatic equalizer, and a newly developed wave difference method (WDM) timing extraction PLL. In order to fabricate the whole circuit on a single-chip LSI, most of the analog portion is organized using; switched capacitor technology. In this paper, the system design concept, circuit configuration, and results of simulations and experiiments are described.