Showing papers by "Michihiko Minoh published in 2011"

Optimizing Mean Reciprocal Rank for person re-identification

Abstract: Person re-identification is one of the most challenging issues in network-based surveillance. The difficulties mainly come from the great appearance variations induced by illumination, camera view and body pose changes. Maybe influenced by the research on face recognition and general object recognition, this problem is habitually treated as a verification or classification problem, and much effort has been put on optimizing standard recognition criteria. However, we found that in practical applications the users usually have different expectations. For example, in a real surveillance system, we may expect that a visual user interface can show us the relevant images in the first few (e.g. 20) candidates, but not necessarily before all the irrelevant ones. In other words, there is no problem to leave the final judgement to the users. Based on such an observation, this paper treats the re-identification problem as a ranking problem and directly optimizes a listwise ranking function named Mean Reciprocal Rank (MRR), which is considered by us to be able to generate results closest to human expectations. Using a maximum-margin based structured learning model, we are able to show improved re-identification results on widely-used benchmark datasets

Cooking Ingredient Recognition Based on the Load on a Chopping Board during Cutting

Abstract: This paper presents a method for recognizing recipe ingredients based on the load on a chopping board when ingredients are cut. The load is measured by four sensors attached to the board. Each chop is detected by indentifying a sharp falling edge in the load data. The load features, including the maximum value, duration, impulse, peak position, and kurtosis, are extracted and used for ingredient recognition. Experimental results showed a precision of 98.1% in chop detection and 67.4% in ingredient recognition with a support vector machine (SVM) classifier for 16 common ingredients.

Developing a Real-Time System for Measuring the Consumption of Seasoning

Abstract: In this paper, we propose a real-time system for measuring the consumption of various types of seasonings. In our system, all seasonings are placed on a scale, and we continuously take images of these items using a camera. Our system estimates the consumption of each condiment by calculating the difference between the weight when the seasoning was picked up and the weight when it was placed back on the scale. Our system identifies the type of seasoning that was used by determining whether or not the seasoning was present on the scale. By using our system, users can automatically log their usage of seasoning. Then, they can adjust the seasoning according to their desired taste.

High Quality Image Coding Method for Gradation Areas by Using Slant Transform

Abstract: We propose a new coding method by applying slant transform (SLT) to gradation areas instead of discrete cosine transform (DCT). A contour in a gradation area, which is often caused by the coding process for high compression, is one of the typical artifact noises for subjective evaluation. Since the pixel value changes linearly in a gradation area, DCT is not always suitable for its transformation. In our proposed method, although only traditional DCT is utilized in the encoding process to avoid increasing computational complexity, the inverse SLT (ISLT) instead of IDCT is performed in the area that is estimated as a gradation area in accordance with the distribution of DCT coefficients in the decoding process. The coding block size of the gradation area is easily expanded by simple operations using the hierarchy of SLT. In accordance with our experimental analysis, the proposed scheme has been performed to clarify the improvement of quality in gradation areas.